System and method for image processing of medical test results using generalized curve field transform

ABSTRACT

A method for image processing medical self-test results receives a digital image of a visual indication of a test result. A digital image is generated of the visual indication of the test result that includes noise and distortions therein. The digital image is processed using generalized curve field transforms to extract relevant features of the digital image in a presence of the noise and distortions to create a transformed image. A diagnosis is generated based upon the transformed image to the plurality of images of the test results.

TECHNICAL FIELD

The present invention relates to image processing technique, and moreparticularly, to image processing of medical self-test results using ageneralized curve field transform.

BACKGROUND

Telemedicine is the use of telecommunication and information technologyto provide clinical health care from a distance. It helps eliminatedistance barriers and can improve access to medical services that wouldoften not be consistently available in distant rural communities. It isalso used to save lives in critical care and emergency situations.Although there were distant precursors to telemedicine, it isessentially a product of 20th century telecommunication and informationtechnologies. These technologies permit communications between patientand medical staff with both convenience and fidelity, as well as thetransmission of medical, imaging and health informatics data from onesite to another. These telemedicine technologies provide convenient waysto obtain care from a healthcare provider, and thus, it is alsodesirable to have a convenient way to fill any prescriptions deemednecessary by the healthcare provider.

One manner of providing telemedicine involves the creation of images oftest results for analysis. However, the test results images may includevarious distortions and issues causing it to be difficult to read. Amanner of improving these distortions and issues would make the resultsmuch easier to analyze.

SUMMARY

The present invention, as disclosed and described herein, in one aspectthereof comprises a method for image processing medical self-testresults receives a digital image of a visual indication of a testresult. A digital image is generated of the visual indication of thetest result that includes noise and distortions therein. The digitalimage is processed using generalized curve field transforms to extractrelevant features of the digital image in a presence of the noise anddistortions to create a transformed image. A diagnosis is generatedbased upon the transformed image to the plurality of images of the testresults.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding, reference is now made to thefollowing description taken in conjunction with the accompanyingDrawings in which:

FIG. 1 illustrates a diagrammatic representation of one embodiment of aimmunoassay test strip;

FIG. 2 illustrates a diagrammatic representation of one embodiment of animmunoassay test wherein an analyte is tested across a plurality of teststrips;

FIG. 3 illustrates a diagrammatic representation of one embodiment of atesting device;

FIG. 4 illustrates a top view of the testing device of FIG. 3;

FIG. 5 illustrates a top view of one embodiment of a testing device;

FIG. 6 illustrates a top view of another embodiment of a testing device;

FIG. 7 illustrates a flowchart of one embodiment of a testing device usemethod;

FIG. 8A illustrates a diagrammatic representation of one embodiment of aprocess for a mobile device application for testing device image captureand image processing, wherein an image alignment indicator is notaligned with the subject of the image;

FIG. 8B illustrates a diagrammatic representation of one embodiment of aprocess for a mobile device application for testing device image captureand image processing, wherein an image alignment indicator is alignedwith the subject of the image;

FIG. 9 illustrates a flowchart of one embodiment of an image analysisprocess using a mobile device;

FIG. 10 illustrates a diagrammatic representation of another embodimentof a process for a mobile device application for testing device imagecapture and image processing, wherein an image alignment indicator isaligned with the subject of the image;

FIG. 11 illustrates one embodiment of a consumer driven biologic anddisease data collection system;

FIG. 12 illustrates one embodiment of a consumer driven biologic anddisease data collection system;

FIG. 13 illustrates an example of a unique biologic ID database table;

FIG. 14 illustrates a flowchart of one embodiment of a biologic datacollection and dissemination process;

FIG. 15 illustrates a perspective view of a system for scanning teststrips;

FIG. 16 illustrates a cross-sectional view of the system of FIG. 15;

FIG. 17 illustrates one embodiment of a vertical flow immunoassaydevice;

FIG. 18 illustrates a cross-sectional view of one embodiment of thevertical immunoassay device of FIG. 17;

FIG. 19 illustrates a color gradient chart;

FIG. 20 illustrates a normalized past tests results rating chart;

FIG. 21 illustrates a mobile device displaying on a screen a mobileapplication variable test functionality;

FIG. 22 illustrates the mobile device of FIG. 21, wherein a housing of atesting device also includes thereon test function indicators;

FIG. 23 illustrates one embodiment of a medical code correlation system;

FIG. 24 illustrates one embodiment of a strep home retail test codestable;

FIG. 25 illustrates one embodiment of a combined pregnancy and Zika homeretail test codes table;

FIG. 26 illustrates flowchart of one embodiment of a medical codecorrelation process;

FIG. 27 illustrates one embodiment of a telemedicine initiation optionwithin a mobile application;

FIG. 28 illustrates another embodiment of a telemedicine initiationoption within a mobile application;

FIG. 29 illustrates one embodiment of a telemedicine conference sessionon a mobile device;

FIG. 30 illustrates a flowchart of one embodiment of a medical filehandoff process;

FIG. 31 illustrates a flowchart of one embodiment of a telemedicineconference initiation process;

FIGS. 32A-B illustrated systems for transmitting prescriptions to apharmacy using telemedicine;

FIG. 33 illustrates an embodiment which uses a mobile application toinform the user which prescriptions have been prescribed

FIG. 34 illustrates an embodiment which uses a mobile application to leta user decide which pharmacy will fill a prescription;

FIG. 35 illustrates an embodiment in which the user can select on amobile application whether to pick up a prescription or have theprescription delivered;

FIG. 36 illustrates a flowchart depicting a process for filling aprescription using a self-diagnostic test and a telemedicine session;

FIG. 37 illustrates an embodiment in which a telemedicine mobileapplication is used to automatically fill a prescription;

FIG. 38 illustrates a mobile device from an embodiment of the system inwhich the user obtains a real-time health insurance quote in response toa self-diagnostic test;

FIG. 39 illustrates an embodiment of the system in which multipleinsurance plans are presented through a mobile application to a user;

FIG. 40 illustrates an embodiment of the system in which more detailedinformation regarding a health insurance quote is presented to a user;

FIG. 41 illustrates a diagrammatic view of a system for providingreal-time health insurance quotes in response to a self-diagnostic test;

FIG. 42 illustrates a flowchart depicting a process for generating areal-time health insurance quote in response to a self-diagnostic test;

FIG. 43 illustrates a side cross-sectional view of an RT-lamp;

FIG. 44 illustrates an RT-lamp with a microfluidic chip disposed in asample chamber;

FIG. 45 illustrates a side view of a smart phone interfaced with amicrofluidic chip;

FIG. 46 illustrates window view of a microfluidic chip;

FIG. 47 illustrates a diagrammatic view of a biofluidic analysis system;

FIG. 48 illustrates a diagrammatic view of an analog testing device to adigital format and unique identifier conversion process;

FIG. 49 illustrates a flow diagram providing visual analysis of medicalself-test;

FIG. 50 illustrates an orientation field for associated images;

FIG. 51 illustrates a flow diagram of the generation of a generalizedcurve field transform;

FIG. 52 illustrates the manner for detecting lines using orientationfields;

FIG. 53 illustrates the general manner for creation of an orientationfield;

FIG. 54 illustrates various alignment spectrum from for orientationfields;

FIG. 55 illustrates a flow diagram of the process for locating teststrips and test pads within a sample image;

FIG. 56 illustrates a kernel used for detecting lines within an image;

FIG. 57 illustrates a located strip within an image;

FIG. 58 illustrates a located test pad within an image;

FIG. 59 illustrates a located strip region within an image;

FIG. 60 illustrates strip region line detection within an image;

FIG. 61 illustrates a block diagram of an image processing process forimproving analysis of testing results;

FIG. 62 illustrates a flow diagram of an image processing processperformed by an image processing system;

FIG. 63 illustrates a flow diagram illustrating image processing toremove noise and other distortions from an image;

FIG. 64 illustrates a flow diagram describing the establishment oforientation lines; and

FIG. 65 illustrates a flow diagram of the process for filteringgeometric shapes from an orientation field.

DETAILED DESCRIPTION

Referring now to the drawings, wherein like reference numbers are usedherein to designate like elements throughout, the various views andembodiments of a system and method for image processing of medical testresults using orientation fields is described. The figures are notnecessarily drawn to scale, and in some instances the drawings have beenexaggerated and/or simplified in places for illustrative purposes only.One of ordinary skill in the art will appreciate the many possibleapplications and variations based on the following examples of possibleembodiments.

Referring now to FIG. 1, there is illustrated one embodiment of animmunoassay test strip 100. The test strip 100 is typically housed in atesting device configured to collect a biologic analyte 106 from a userand to direct to the biologic analyte 106 onto the testing strip 100.However, it will be understood that the biologic may be applied onto astrip 100 without the strip 100 needing to be within a testing device.The test strip 100 includes a backing 102. The test strip 100 is made upof multiple sections disposed on the backing 102. A sample pad 104 isdisposed on one end of the strip 100, for collecting the biologicanalyte 106. The biologic analyte 106 may be any biologic needed for usein the immunoassay, such as urine, blood, saliva, stool, sweat, or otherbiologics to be used as an analyte. Various methods may be used toacquire the needed biologic, and such may be provided to the userpackaged with the test, such as swabs, vials, containers, dilutants andother solutions, or any other equipment required. In the case of a bloodanalyte, a few drops of blood may be obtained from a finger stick usinga finger prick device. Such a blood analyte may be blood mixed with anadequate amount of buffered solution to create the sample analyte 106 ora blood sample that is not diluted or otherwise manipulated, in whichcase the blood only is the analyte 106.

The biologic analyte 106, after coming into contact with the sample pad104, begins to migrate across the strip 100 by capillary action, cominginto contact with other sections of the strip 100. A particle conjugatepad 108 is disposed between the sample pad 104 and a test line 110. Theconjugate pad 108 may contain various reagents associated with aparticular antigen, such as a virus, allergen, or bacteria, the reagentsbeing items such antibodies, enzymes, or other reagents needed todiagnose the particular condition. The reagent in the conjugate pad 108may be conjugated with particles of materials such as colloid gold orcolored latex beads. As the analyte 106 migrates through the conjugatepad 108, antibodies present in the sample analyte 106 complex with thereagents in the conjugate pad 108, thereby creating an immune complexthat will migrate to the test zone or test line 110.

The test line 110 (T) may be precoated with the relevant antigen inquestion, i.e., a virus, allergen, or bacteria, for the detection ofantibodies associated with the particular antigen. The immune complexcreated when the analyte 106 passes through the conjugate pad 108 iscaptured onto the antigen contained on the test line 110. This maycreate a qualitative response on the strip where the test line 110 islocated, such as a colored response. In some embodiments, the test line110 may not be a line, but may be other shapes or symbols, such as aplus sign. If no antigen-anti-antigen complexes are present in theanalyte, no reaction occurs in the test line 110 and a qualitativeresponse will not occur.

After passing through the test line 110, the analyte migrates furtheralong the strip to reach a control line 112, where excessanti-antibody-colloidal gold or latex conjugates get bound. Aqualitative response may be shown at the control line 112, indicatingthat the sample has adequately migrated across the testing membrane orsubstrate as intended. It will be understood that the control line 112is not necessarily needed to perform the test, and may be eliminatedentirely, but the control line 112 does provide a comparative examplefor a user reading the test. For example, the control line 112, inembodiments where a colored qualitative response is provided, may appearas an overly saturated color, such as a dark or bright saturated red,once the sample reaches the control line 112. This saturated color maybe used as a comparison against the qualitative response shown on thetest line 110. For example, if the qualitative response shown on thetest line 110 is a much lighter red than that on the test line 110, itmay be that very little reaction occurred at the test line. Of course,if no response is shown at all at the test line 110, no reaction hasoccurred. If the qualitative response at the test line 110 is of asimilar saturation to the control line 112, a strong reaction isindicated.

The strip 100 may not be a continuous substrate. Rather, the varioussections of the strip 100 may be separate from each other, but alladhered to the backing 102. As shown in FIG. 1, the sample pad 104 andthe conjugate pad 108 are separate structures from each other. The testline 100 or zone and the control line 112 or zone are both disposed aspart of a nitrocellulose membrane strip 114. The nitrocellulose membranestrip 114 is also adhered to the backing 102, but separate from thesample pad 104 and the conjugate pad 106. As shown in FIG. 1, the end ofthe sample pad 104 adjacent to the conjugate pad 106 may overlap theconjugate pad 106, with that end of the sample pad 106 lying over theadjacent end of the conjugate pad 106. Similarly, the end of theconjugate pad adjacent to the nitrocellulose membrane strip 114 may lieover the end of the nitrocellulose membrane adjacent to the conjugatepad. This allows for the analyte 106 to be more easily deposited ontoeach section of the strip 100 as it migrates across the strip 100. Afterthe analyte 106 migrates across the nitrocellulose membrane strip 114,and thus across the test line 110 and the control line 112, the analyte106 comes into contact with a wick 116 for absorption and collection ofthe analyte 106. The end of the wick 116 adjacent to the nitrocellulosemembrane strip 114 may lie over that adjacent end of the nitrocellulosemembrane strip 114, as shown in FIG. 1.

Several Flow Immune Assays have been directed toward identifyingproteins, molecules of interest, and even immunoglobulins IgG, IgA, andIgM. IgE is an antibody (immunoglobulin E) that is normally present inthe blood freely circulating until it moves into the tissue where it isbound to mast cells through the receptor FcERI (F-C-epsilon-R-one)otherwise known as the high affinity IgE receptor. There is a smallamount of IgE bound to IgE receptors (high and low affinity receptors)on basophils, eosinophils, and other cells in the blood and tissues.

Many assay systems are geared toward the detection of infectiousproteins. All of the aforementioned tests use a non-humanantibody—usually IgG type—e.g., goat IgG antibody directed against aprotein of interest to detect the protein of interest from the sample(blood, urine, saliva, sweat, etc.). This antibody complexes withprotein of interest and forms a complex that travels across the membraneuntil it reaches the test zone. In the test zone there is an IgG typeantibody directed against IgG from that species of animal. As furtherdescribed herein, the present detecting apparatus and method use human(patient/consumer-derived) antibodies from the sample and the test zonethat contains a humanized antibody directed against the protein ofinterest that is preconjugated to a detecting substance that results ina visual change.

Summary of Target Antigen:

-   -   The target antigens may be proteins, glycoproteins, lipoproteins        or other molecular substances capable of eliciting an immune        reaction and/or being bound by human specific IgE (sIgE).

Immune Assay to Detect Specific IgE:

-   -   In the detecting apparatus and method of using the same, the        antigens are proteins conjugated to a noble metal, for example,        gold, or latex conjugated to antigen in the test zone, for the        purpose of detecting the presence of specific IgE (e.g.,        anti-peanut IgE in a blood sample from a finger prick). For        example, an IgG class antibody (IgG1, IgG2, IgG3, or IgG4) or        fragments of those classes of antibodies (fab fragments) whose        origin may be any animal species (goat, rat, human, etc.)        capable of detecting human IgE (anti-IgE IgG)—a suitable        commercially available humanized antibody, such as omaluzimab        may be used—may be used to form immune complexes of        IgG-anti-IgE-sIgE that will migrate to the test zone having        selected specific IgE that can bind to the conjugated antigen.

Immune Assay to Detect Total IgE (not Concerned about Specific IgE):

-   -   Another embodiment includes using an IgG class antibody (IgG1,        IgG2, IgG3, or IgG4) or fragments of those classes of antibodies        (fab fragments) whose origin may be any animal species (goat,        rat, human, etc.) capable of detecting human IgE (anti-IgE        IgG)—a suitable commercially available humanized antibody that        is preconjugated to a detecting molecule that results in a color        change when bound to IgE as the target antigen in the test zone.

Referring now to FIG. 2, there is illustrated one embodiment of animmunoassay test 200 wherein an analyte 202 is tested across a pluralityof test strips 204. The plurality of test strips 204 may each beconfigured for testing for a particular antigen. For instance, one stripmay be for testing for the presence of streptococcal bacteria (strepthroat), one strip may be for testing for a peanut allergy, one stripmay be for testing for the Zika virus, etc. Additionally, each strip mayalso test for multiple antigens. For example, as shown in FIG. 2,multiple testing panels or lines maybe be incorporated. Each line may befor a particular antigen. As shown in FIG. 2, multiple test lines 206,208, and 208 may be disposed along the plurality of strips 204. A striptesting for allergens may have a panel for testing for peanut allergiesshown at test line 206 (CH1), for cat allergies shown at test line 208(CH2), or grass allergies shown at test line 210 (CH3).

Other examples of configurations for the testing panels can be, but arenot limited to: 1) Food 5: Peanut, milk, soy, wheat, egg; 2) Nut andseed panel: almond, cashew, hazelnut, peanut, pecan, walnut, sesameseed, sunflower seed; 3) seafood: crab, lobster, shrimp, salmon, tuna;4) Pets: cat, dog; 5) Indoor allergens: dust mites, mold mix(Alternaria, Aspergillus, Penicillium, Cladosporium), cat, dog; and 6)seasonal allergens: grass (Bermuda, bahia, Johnson, rye, timothy), trees(oak, elm, cedar, mesquite, pine, etc.), weeds (pigweed, ragweed, sage,Russian thistle).

With respect to other non-allergen antigens, the panels may be fortesting for strep, Zika, flu, anthrax, cold viruses, cancer, HPV, Lymedisease, mononucleosis (mono), and other illnesses, and/or otherconditions such as pregnancy (hCG detection) and disease risks. Someembodiments may allow for the testing of various arboviruses(arthropod-borne viruses). Arboviruses are viruses that are transmittedby arthropods, with mosquitos being a common vector for the virus.Vectors are organisms that transfer the virus from a host that carriesthe virus. Thus, in the case of mosquitos, a mosquito that feeds on ahost that is infected with a virus may infect others when that mosquitoagain feeds on an uninfected host. Well-known arboviruses include Denguevirus, Japanese encephalitis virus, Rift Valley fever virus, West Nilevirus, yellow fever virus, chikungunya, and Zika virus. Urine, blood,and saliva and other biologics may be used for arboviruses testing.

Certain antigens or medical conditions may be logically paired together.For instance, a testing device may include both a strip for detection ofpregnancy and a strip for the detection of the zika virus, as the Zikavirus has been known to cause birth defects in infants born to pregnantwomen that are infected with Zika. Thus, combining these two tests intoa single testing device or kit would alert a woman to a potential Zikainfection proximate in time to the time she also discovers she ispregnant, allowing the woman to seek medical attention immediately. Thisis a substantial improvement over past Zika testing, where a woman maybe required to wait weeks before results are returned from a lab afterhaving the biologic collected by her physician. In many cases, this maylead to a woman having passed a state-mandated cutoff point forabortions, such as 24 weeks in some states. Combining a Zika test with apregnancy test and physically linking the two tests, and thus allowingfor a woman to determine a Zika risk at the time of taking a pregnancytest, in which a pregnancy test may be taken as soon as six days afterconception, allows for that woman to take action much sooner than thestate mandated cutoff and waiting for lab results would allow.

Various testing devices that include the test strip 100 or strips may beused, such as a slide that supports the test strip 100, a cassette baseddiagnostic test, a dipstick, or combinations thereof. The test resultsin various embodiments may be in the form of a visual qualitativereading test, a visual semiquantitative format, a reader quantitativeassay format, and/or combinations thereof. Additionally, an electronicimplementation may be used where the result is displayed digitally on ascreen disposed within the apparatus, and visible to the user.

The apparatus and method of detection may be a “one-step” approach fromsample to reading without sample dilution or other sample manipulation.The sample may be diluted or endure other sample manipulation, forexample the blood sample is diluted with a buffer.

Referring now to FIG. 3, there is illustrated a diagrammaticrepresentation of one embodiment of a testing device 300. The testingdevice 300 includes a housing 302 that forms the body of the testingdevice. The housing 302 may be made of plastic, metal, or any materialdurable enough for shipping and subsequent handling by a user. Thehousing 302 may be hollow so that a plurality of test strips 304 may behoused within and so that a biologic may be deposited within the housing302. The testing device 300 may further have a plurality of windows 306,each window being associated with one of the plurality of test strips304, and allowing for a user to view at least the section of thenitrocellulose membrane strip 114 where the test line 110 and controlline 112 are located. The plurality of windows 306 may be open, orcovered with plastic, glass, or other materials that allow for viewingthe plurality of strips 304. A sample well 308 may be disposed on asurface of the housing 302 to allow a user to deposit a biologic intothe housing 302. The sample well 308 would be disposed over or near thesample pad 104 of the test strip or strips 100. In the embodiment shownin FIG. 3, a single sample well 308 is included for collection of asingle type of biologic for testing, with each of the plurality ofstrips 304 being suited for testing for antigens using that particularbiologic sample type. For example, if the testing device 300 is acombined pregnancy and Zika test, having both a pregnancy strip and aZika strip, a urine sample may be deposited into the sample well 308,causing the urine sample to come into contact with the sample pad 104 onboth the pregnancy strip and the Zika strip. It will be understood thatboth of these tests may also be performed with a blood sample.

The testing device 300 may also have disposed on the surface of thehousing a crosshair symbol 310, used as an alignment target. This symbolmay be a graphic printed or adhered to the testing device 300. Thecrosshair symbol 310 is used to align the testing device 300 for thetaking of an image of the testing device 300 using a camera on a mobiledevice, for use in a mobile device application described herein. Inother embodiments, the crosshair symbol 310 may be other types ofsymbols, such as a simple shape (circle, square, etc.), other images(such as a medical cross symbol, an arrow, etc.), or any other type ofimage.

Referring now to FIG. 4, there is illustrated a top view of the testingdevice 300. There is again shown the housing 302, the plurality of teststrips 304, the plurality of windows 306, the sample well 308, and thecrosshair symbol 310.

Referring now to FIG. 5, there is illustrated a top view of oneembodiment of a testing device 500. The testing device 500 includes ahousing 502 having a plurality of test strips 504 within the housing 502and a plurality of windows 506 for display of the plurality of strips504. The housing 502 also includes a plurality of sample wells 508disposed on one side of the testing device 500. Each of the plurality ofsample wells 508 is associated with one of the plurality of test strips504 and each of the plurality of sample wells 508 may be disposed overone of the sample pads 104 on the associated one of the plurality oftest strips 504. This allows for a biologic to be deposited into each ofthe plurality of sample wells 508, with each well 508 serving totransfer the biologic to the test strip underneath the sample well. Thetesting device 500 further includes a crosshair 510. The crosshairsymbol 510 is used to align the testing device 500 for the taking of animage of the testing device 500 using a camera on a mobile device, foruse in a mobile device application described herein.

Referring now to FIG. 6, there is illustrated a top view of anotherembodiment of a testing device 600. The testing device 600 includes ahousing 602 having a plurality of test strips 604 within the housing 602and a plurality of windows 606 for display of the plurality of strips604. The housing 602 also includes a plurality of sample wells 608. Inthis embodiment, the sample wells are located on different ends of thehousing 602. In the case of a two test strip device, the sample wells608 are disposed on opposite ends of the testing device 600. The strips604 would be arranged within the housing in such a way as to allow thesample pad 104 on each of the strip to be disposed underneath one of thesample wells 608. This is useful for testing devices that requiredifferent biological samples. For example, if the testing device 600required a urine sample for one strip and a blood sample for the otherstrip, having the wells 608 disposed on opposite sides of the testingdevice would reduce the likelihood that a urine sample, for instance,might be inadvertently deposited into the well designated for the bloodsample. In embodiments where there are more than two strips, and morethan two wells, the well positions may alternate between the two sidesof the testing device. For instance, a first well for a first stripmight be disposed on the left side of the testing device, a second wellfor a second strip might be disposed on the right side of the testingdevice, a third well for a third strip might be disposed on the leftside of the testing device, a fourth well for a fourth strip might bedisposed on the right side of the testing device, and so on. The testingdevice 600 further includes a crosshair 610. The crosshair symbol 610 isused to align the testing device 600 for the taking of an image of thetesting device 600 using a camera on a mobile device, for use in amobile device application described herein.

The diagnostic test can, for example, be produced in a various formatsfor different users, such as, but not limited to, consumer/in-home usewhere the test is purchased through retail channels which will allowindividuals to get an immediate, cost-effective test result that canlead to specific avoidance and treatment through follow-up with amedical professional.

The diagnostic test can be provided to and used by hospitals and clinicsto provide rapid, on-site test results that are required to prescribecertain medications, such as omaluzimab, by their FDA labels.

This diagnostic assay can be modified to detect the presence of specificIgE in pets.

It is also noted that housing 602 is designed such that both strips 604are disposed in physical proximity thereto and in the same actualhousing. In this manner, both sets are linked physically to each othersuch that they cannot be separated and can be associated with a singleindividual and the actual test cannot be separated. As such, when apatient applies the specimens to the two areas 608 and the test resultsare exhibited, there is a high probability that two tests were performedat the same time associated with the same patient. Additionally, andelectronic chip (not shown) can be embedded within the housing 602 suchthat the housing 602 can be registered to a specific patient andassociated with the medical records of that patient.

Referring now to FIG. 7, there is illustrated a flowchart of oneembodiment of a testing device use method 700. The method 700 begins atstep 702 where a biologic is collected in a sample well or wells of atesting device. The biologic collected may be a non-diluted ornon-manipulated biologic, such as blood, urine, or saliva from the userof the test. Diluted or manipulated biologics may be used instead, asrequired by the specific test. For example, if a viral test requires thebiologic to be added to a solution, the biologic could be added to asolution that has previously been placed in a sterilized vial providedwith the testing device. After the required amount of time has passed,the solution containing the biologic could be deposited into the well orwells. At step 704, the biologic contacts a sample pad disposed on astrip or strips within the testing device. At step 706, the biologicmigrates along the strip or strips to come into contact with a conjugatepad containing antibodies. Antibodies present in the biologic willcomplex with the antibodies in the conjugate pad to create an immunecomplex. At step 708, the biologic migrates into a test zone of thestrip or strips, coming into contact with an antigen. The antibodies inthe conjugate pad serve to provide a means of detection, such as acolored response, if the immune complex binds with the antigen presentin the test zone of the strip. At decision block 710, binding of theantibodies with the antigen may or may not occur depending on ifantibodies associated with the antigen are present in the biologic ornot. If a binding between the antibodies and the antigen does not occurthe process moves to step 712 where no qualitative response is producedon the test line. If a binding does occur, at step 714 a qualitativeresponse is produced on the test line. Whether a binding occurs or not,and whether a qualitative response is produced or not, the process movesto step 716 where the biologic migrates into a control zone of the stripor strips where excess conjugates get bound and produces a qualitativecontrol zone reaction indicating that the sample has adequately migratedacross the testing zone.

It will be understood by one skilled in the art that the antibodies andantigens applied to the testing strip may be altered depending on thetype of condition being tested. For example, in the case of testing formedical conditions that do not involve an illness or infection, likepregnancy, and thus the sample biologic does not contain antibodiesassociated with the condition, antibodies that react to markers beingtested for may be applied to the testing strip instead of an antigen.For instance, pregnancy test requires testing for the presence of hCG.Since hCG is a hormone and not an antibody produced in response to aninfection, the testing strip may have antibodies that will react to thepresence of hCG applied to the testing zone or line of the testingstrip, as well as to the conjugate pad. Similarly, some tests mightrequire antibodies be applied to the testing strip to detect antigenspresent in the sample, rather than antibodies.

Referring now to FIGS. 8A and 8B, there is illustrated a diagrammaticview of one embodiment of a process 800 for a mobile device applicationfor testing device image capture and image processing. The mobile deviceapplication allows for an image of a testing device, such as testingdevice 300, to be captured using a camera installed on a mobile device802 having a screen 804. While the mobile device 802 displays on thescreen 804 the scene captured by the camera, the mobile deviceapplication also displays a graphic on the screen 804 in the form of aboxed outline 806, the size of the outline 806 corresponding to the sizeof the testing device 300. Also displayed on the screen of the mobiledevice 802 within or near the outline is a crosshair graphic 808. A userof the mobile device 802 attempts to align the outline 806 with theborders of the testing device 300 and also attempts to align thecrosshair graphic 808 with the crosshair 310 on the testing device 300.While alignment has not yet been achieved, a misalignment warning 810may appear on the screen of the mobile device 802, indicating to theuser that alignment has not yet been achieved. Such is shown in FIG. 8A.

In FIG. 8B, there is shown the result of a successful alignment of theoutline 806 with the testing device 300 and successful alignment of thecrosshair graphic 808 with the crosshair 310 on the testing device 300.As shown in FIG. 8B, once aligned, a success indicator 812 may appear,such as a check mark or other positive status symbol, on the alignedimage. Successful alignment causes the camera on the mobile device 802to capture the current image of the testing device 300. Other checks mayoccur, including ensuring that the image is focused before the image issaved. This image is then processed to determine a result based on theseverity of the reaction occurring on the test strip. The mobile deviceapplication performs an analysis of the test line captured in the image,counting the number of colored pixels, as well as determining theintensity of the color. The mobile device may then compare this numberand color intensity to that in the control line, providing amathematical evaluation of the test line. Utilizing unique wavelengthsof light for illumination in conjunction with either CMOS or CCDdetection technology, a signal rich image is produced of the test linesto detect the colloid gold or latex particles. This provides anadvantage because a user simply looking at the two lines may not knowwhat the test line indicates, such as when the colored line does appearson the strip, but it is a faded line, rather than a dark line. Based onthis analysis, the mobile device application may provide a resultsindicator 814.

The results indicator 814 may be a qualitative result or a quantitativeresult. For example, and as shown in FIG. 8B, a qualitative result forthe results indicator 814 may indicate, in the case of a testing devicefor testing pregnancy as well as an infection, a plus sign next to aline reading “pregnant:” and a plus sign next to a line reading“infection:” to indicate that the user is both pregnant and infectedwith the bacteria or virus being tested, such as the Zika virus. For aquantitative result, the results might provide a numeric rating. Forinstance, a rating system between 1-100 may be used. If the resultsprovide a low rating to the user, such as a rating of 10, this indicatesa low risk of infection, although medical attention may be sought by theuser anyway. For example, if the user is pregnant, and also receives a10 rating for Zika, this may indicate that Zika was detected in lowamounts. However, the user may still seek medical attention or furthertesting from her doctor because Zika has been known to cause birthdefects. If the rating is a high rating, such as 95, this indicates thatan infection has most likely occurred and medical attention should besought immediately.

This same quantitative rating system may be applied to any test (viralinfections, bacterial infections, pregnancy, and other healthconditions), as the quantitative test can be performed using thesoftware described herein to accurately test bound antibodyconcentrations on the test strip. In some embodiments, a combinedqualitative and quantitative result may be presented, such as both arating and a plus or minus sign being presented, or other types ofquantitative and qualitative indications. Additionally, variouscombinations of tests may be provided for in the testing device, such aspregnancy/Zika, pregnancy/flu, pregnancy/strep/Zika, etc.

Referring now to FIG. 9, there is illustrated a flowchart of oneembodiment of an image analysis process 900 using a mobile device. Atstep 902 a mobile device application is launched. At step 904 a cameraon the mobile device is activated and a crosshair indicator and atesting device outline appear on the mobile device screen. At step 906the crosshair indicator presented on the screen of the mobile device isaligned with a crosshair icon on the testing device and the deviceoutline presented on the screen of the mobile device is aligned with theborders of a testing device. At step 908, an indicator of successfulalignment is presented on the screen and an image of the testing deviceis taken by the mobile device camera. At step 910, the mobile deviceapplication processes the image of the testing device to determine testline strength by counting the number of colored pixels contained in thetest line. At step 912, the mobile device application correlates lineintensity with analyte concentrations to further determine test linestrength. At step 914, the mobile device application presents the testresults based on pixel count and line intensity, providing either aqualitative or quantitative result.

In some embodiments, the number of pixels indicating bound antibodies onthe strip may be measured against that in the control line to compareline intensity between the two lines, with the control line acting as anexample of a strong reaction, indicating a strong infection, anddetermining how close the test line intensity is to the control line.This would lead to a logical quantitative result. For instance, if thetest line is determined to have a pixel count and line intensity that is25% of the pixel count and line intensity of the control line, a ratingof 25 may be given. If a qualitative result is to be provided, a ratingof 25 may give a qualitative result that is negative, or it could bepositive depending on the type of condition being tested and knownactual infection results where a rating of 25 occurred for thatcondition.

In some embodiments, the test line may not be compared with the controlline to determine a result. Rather, the mobile device application mayhave access to a database having data on numerous past tests for thesame condition. This data may instead be used as the control. Thisallows the application on the mobile device to retrieve data on pasttests and compare the test line data of the current test to past tests.Overall data for past tests may be provided and compared against, suchas providing an average or a grading curve of past tests, or individualtests rated as having accurate results may be compared against.

In addition to a status result of an infection or other medicalcondition being provided to the user, other indicators of health mayalso be tested and results thereon provided. This provides for potentialearly identification of pregnancy and risk of morbidity, allowing formedical attention to be sought much more quickly. Indicators of healthmay be detected from biologics, such as urine and blood. Urine, forexample, allows for the detection of glucose levels, proteins, bacteria,and infectious markers. In the case of glucose, glucose is usually notfound in urine, but, if it is, that is an indicator of extremely highlevels of glucose in the body, where the kidneys release excess glucoseinto urine. This is often a sign of diabetes. Protein in the urine mayindicate a malfunctioning of the kidneys, which could be the result ofhigh blood pressure. Similarly, if blood is detected in urine, it couldbe a sign of a problem with the kidneys or the bladder. Blood, forexample, allows for the detection of glucose, inflammation, hormones,genetic defect risks, and metabolic endocrine disorders.

Referring now to FIG. 10, there is illustrated another embodiment of asuccessful alignment of the outline 806 with the testing device 300 andsuccessful alignment of the crosshair graphic 808 with the crosshair 310on the testing device 300, wherein quantitative results for health riskindicators are provided. In this embodiment, the results indicator 814provides a qualitative result for pregnancy, and quantitative resultsfor other health risk indicators. In the embodiment shown in FIG. 10,the health risk indicators being tested are markers for blood pressureand for glucose levels. For blood pressure, this is a test for markersin the blood that can be associated with high blood pressure. Thesecould be test for such things as low levels of vitamin D and the such.Studies have shown that patients suffering from essential hypertensionwill be under oxidative stress and Malondialdehyde (MDA) is theprincipal and most studied product of polyunsaturated fatty acidpre-oxidation. This can show an indirect correlation with anti-oxidants,particularly with superoxide dismutases (SODs) (r=0.573) and catalase(r=0.633) representative anti-oxidant are involved in reducing thestress of a patient's biological system during hypertension. Anothermarker for hypertension is buildup of uric acid, where in uric acid is amarker for xanthine oxidase-associated oxidants and that the lattercould be driving the hypertensive response. Additional markers arecortisol, a hormone. The test strips 604 can test for the differentbiological markers.

The results indicator 814 provides numeric ratings, in this case, 1-100,with the blood pressure rating being 88 and the glucose rating being 95.This indicates that both blood pressure and glucose are extremely high.Due to this, an additional alert indicator 1002 is presented to the useron the screen of the mobile device, alerting the user to seek medicalattention immediately. This is to ensure that the health of both thepregnant woman and the fetus can be checked as close to the time ofpregnancy detection as possible and medical attention received ifneeded.

Referring now to FIG. 11, there is provided a flowchart of oneembodiment of a pregnancy disease risk assessment process 1100. Theprocess 1100 begins at step 1102 where a biologic is collected anddeposited in a testing device for testing of the biologic. At step 1104,the biologic is processed by the testing device for detection ofpregnancy and various other medical conditions. These medical conditionsmay be high blood pressure, diabetes, bacterial or viral infections,inflammation, an increase in hormone levels, genetic disease markers,and/or metabolic disorders. At step 1106, a mobile device is used tocapture an image of the testing device after testing is complete. Insome embodiments, test results may be immediate. In other embodiments,and depending on the medical conditions being tested, the test may takea certain amount of time to complete. In this case, the user of the testwould be alerted to this fact in instructions provided with the testingdevice. Additionally, a visual indicator on the testing device may alertthe user that a test is now complete. At step 1108, the mobile deviceprovides a rating for each medical condition being tested, such as thatdescribed with respect to FIG. 10 herein.

At decision block 1110, it is determined whether the ratings for eachcondition exceed a certain threshold for that condition. If not, theprocess 1100 moves to step 1112, where an indication is presented to theuser via the mobile device screen that medical attention is notcurrently advised or necessary. If at step 1110 it is determined that atleast one of the medical conditions being tested rises above a certainthreshold, the process 1100 moves to step 1114 where a warning ispresented to the user via the mobile device screen that medicalattention is advised. The thresholds for medical conditions may nottrigger a warning even if a rating exceeds a threshold, if, in the eventof multiple tests being performed, the combined test results do notwarrant immediate medical attention. For example, if a user is testingfor a cold virus, blood pressure, and glucose, and only the cold virusrating is above the threshold, there may not be a warning provided tothe user. Additionally, ratings may be weighted or aggregated based onthe medical conditions being tested. For example, if blood pressure,inflammation, and glucose are being tested for, and they all are givenonly moderate ratings that do not rise above the threshold for anycondition individually, an warning to seek medical attention may stillbe provided due to the combination of conditions taken together.

Referring now to FIG. 12, there is illustrated one embodiment of aconsumer driven biologic and disease data collection system 1200. Datacollected from users performing the tests disclosed herein effectivelycan provide a wealth of information. As tests are performed data may bepassed by a plurality of mobile devices 1202 being used by usersperforming tests to a database 1204, the database being at a remoteserver 1206, over a network 1208. The user is sourcing a biologic fromuser's own body. This is done at home, not in a lab, hospital, orclinic. Each particular test would expect a certain type of biologic tobe provided. For instance, for a pregnancy test, a urine sample isprovided and tested for pregnancy markers. For a stool test, the stoolmight be dissolved in a vial with a solution provided with the testingdevice/kit, and tested for various disease or infectious markers. Dataand results from the tests may be stored on the database 1204 at theremote server 1206. As described herein, this data may be used as acontrol for testing analysis for users of the plurality of mobiledevices 1202. This data may also be used to provide data sets forbiologics to a medical organization 1210. The medical organization 1210may be doctor's offices, researchers, hospitals, testing labs, and otherindividuals or organizations that have an interest in the health andanalysis of users taking the test and of their biologic samples. In thisway, data can be gathered from a variety of biologics tested for avariety of different medical conditions and characteristics.

Referring now to FIG. 13, there is illustrated an example of a uniquebiologic ID database table 1300. The table 1300 is illustrative of thetype of data stored in association with data for a biologic transmittedby the plurality of mobile devices 1202 for storage on the database1204. A biologic ID header 1302 is provided that shows that the biologicsample has been given a unique ID. All data concerning the biologic maybe stored in association with the unique biologic ID. The table 1300also includes a biologic type entry 1304. This designates what type ofbiologic that the biologic associated with the unique ID is, such asblood, urine, stool, saliva, sweat, or other biologics. The table 1300also provides a plurality of test ratings 1304, for various testsperformed on the biologic. In the example shown in FIG. 13, a bloodbiologic is provided having an assigned ID of 2402, and having beentesting for pregnancy markers, the Zika virus, and for glucose levels.The rating for pregnancy was a 99 rating, the rating for a Zikainfection was a 75, and the rating for glucose levels was a 10. Thiswould indicate that the test subject has an extremely high likelihood ofboth a pregnancy and a Zika infection, which would have resulted in awarning to seek medical attention at the conclusion of the tests. Otherinformation may also be stored in the database in relation to thebiologic, including other condition ratings, time and date each test wasperformed, user information such as ethnicity, gender, and age, andstatus indicators such as whether a test subject visited a physician asa result of the tests. The database 1204 thus provides the test subjectwith a growing collection of information that may be accessed by thetest subject. This allows the test subject to present the test resultsto her physician for medical attention or additional testing, and allowsfor others who may access the database, such as disease researchers, tohave access to data on various biologic samples and their markers.

Referring now to FIG. 14, there is illustrated a flowchart of oneembodiment of a biologic data collection and dissemination process 1400.The process 1400 begins at step 1402 where a user of a testing devicecollects a biologic sample for use in a test or a series of tests. Atstep 1404, the test or series of tests are performed on the biologicsample. At step 1406, a mobile device application checks the biologicsample the testing device result to determine a quantitative result ofthe test to provide a correlative value for the condition being testedin the biologic sample. At step 1408, the test results and correlativevalues, or multiple values if multiple tests on the biologic sample wereconducted, are transmitted to the remote server 1206. At step 1410, thebiologic sample is given a unique identification number in the database1204. At step 1412, the test results and correlative value or values arestored in the database 1204 in association with the uniqueidentification number given to the biologic sample collected and inassociation with the particular tests performed. This way, theparticular biologic sample may have various characteristics stored andretrieved in association with the biologic sample, and the test resultsmay also be retrieved when data on a particular test is needed on across-section of users.

At step 1414, the results are provided to the user on the user's mobiledevice. At step 1416, the results are provided to the user's healthcareprovider. The healthcare provider may receive the test results due to avisit from the user, the user bringing the results of the test with heron her mobile device, or the healthcare provider may receive the resultsfrom the database 1204 if the healthcare provider has permission toaccess the database 1204, or if access is granted in anticipation of theuser's appointment with the healthcare provider. At step 1418, the testresults are also provided to other healthcare industry individuals andorganizations, including medical researchers, hospitals, and others.

Referring now to FIG. 15, there is illustrated a perspective view of asystem for scanning test strips. The housing 604, as describedhereinabove with respect to FIG. 6, is illustrated as being disposedwithin a slot 1502 in a test housing 1504. The test housing 1504 isconnected through a line 1506 to a PC 1508. When the housing 604containing the test strips 604 after being subjected to the biologics isinserted within the slot 1502, the test housing 1504 will scan the teststrips 604 and analyze the results with the PC 1508. The PC 1508 willrun some type of algorithm that can analyze the results of both of thetest strips 604. There can be provided to indicators 1510 and 1512 onthe surface of the test housing 1504, one being, for example, a readyLED and one being a green LED. The PC 1508, after analyzing results, canthen provide a warning indicator such as lighting up the green LED for apositive indication of pregnancy and the red LED for indicating thatthere is some issue. As an example, suppose that the second test striptested for the Zika virus. If so, a warning would be appropriate tooutput and activate the red LED. There could be any other type ofindicator associated with the second test at 604 that, in a combinationwith the test results of the first test strip, i.e. for testing for thepresence of a pregnancy state, testing for such things as diabetes, etc.Further, although only two test strips 604 are illustrated, there couldbe multiple test strips testing for many different biological issuesthat may be present in an individual. In this embodiment, by insertingthe housing 602 into the test housing 1504 and allowing the PC 1508 toanalyze the results, the indicators associated with the test strips canbe analyzed with the assumption that all of the test return results wereassociated with an individual and in proximate time to each other. Thatmeans that the individual patient applied biological specimens, such asurine, blood, etc., to the appropriate test strips and, since these wereall contained within the same test strip housing 602, this provides anindication that they are associated with a single patient. Further, thetest performed will typically be a test that will provide a veryshort-term response. Thus, the specimens can be applied to the teststrips 604 in the test strip housing 602 and then inserted within theslot 1502 for testing by the PC 1508.

Referring now to FIG. 16, there is illustrated a cross-section of thetest housing 1504. It can be seen that the test strip housing 1602 ispassed in slot 1502 past the camera 1602. The camera 1602 is operable toscan a small cross-section of the test strips 604 on the surface thereofas the test strip housing 602 passes thereby. Of course, there couldalso be a much larger camera provided for taking an entire image of thetest strips 604 after being inserted within the test housing 1504. Thecamera 1602 is connected via a wire 1604 two in electronics package 1606to process the information and send it to the PC 1508. The electronicspackage 1606 will also drive the indicators 1510 and 1512.

Referring now to FIG. 17, there is illustrated one embodiment of avertical flow immunoassay device 1700. It will be understood thattesting device 300 and other embodiments herein illustrate a lateralflow immunoassay device. However, other types of immunoassay devices maybe used. For example, vertical flow immunoassay devices may be used, atwo-sided flow through assay, or even a sandwich ELISA test may be run.

The testing device 1700 includes a housing 1702 that forms the body ofthe testing device. The housing 1702 may be made of plastic, metal, orany material durable enough for shipping and subsequent handling by auser. The housing 1702 may be hollow so that a plurality of immunoassaytest pads 1704 may be housed within and so that a biologic may bedeposited within the housing 1702. The testing device 1700 may furtherhave a plurality of sample wells 1706, each sample well having one ofthe plurality of immunoassay test pads 1704 disposed within, andallowing for a user to view at least a section of a nitrocellulosemembrane of each of the immunoassay test pads 1704, the membrane 1708having a test line 1708 and control line 1710. The testing device 1700may also have disposed on the surface of the housing a crosshair symbol1712, used as an alignment target. This symbol may be a graphic printedor adhered to the testing device 1700. The crosshair symbol 1712 is usedto align the testing device 1700 for the taking of an image of thetesting device 1700 using a camera on a mobile device, for use in amobile device application described herein. In other embodiments, thecrosshair symbol 1712 may be other types of symbols, such as a simpleshape (circle, square, etc.), other images (such as a medical crosssymbol, an arrow, etc.), or any other type of image. In otherembodiments, the device 1700 may be configured in such a way as to allowa biologic sample to be deposited into a sample well, and to present theresults of the test on the opposite side of the housing. Such aconfiguration would allow the biologic to flow through the testing padwithin the housing, with the reaction occurring on a reactive membraneon the side of the device opposite the sample well, with the devicehaving a window for viewing the results.

Referring now to FIG. 18, there is illustrated a cross-sectional view ofone embodiment of the vertical immunoassay device 1700. There is shownone of the plurality of immunoassay test pads 1704 residing within thehousing 1702 below one of the plurality of sample wells 1706. The one ofthe plurality of immunoassay test pads 1704 includes a immunoreactivemembrane 1802, such as the nitrocellulose membranes disclosed herein.The immunoreactive membrane 1802 may have particle conjugates disposedthereon that binds when a biologic sample is received onto theimmunoreactive membrane 1802 via the sample well 1706, if the biologicsample contains the antigens or antibodies, or other indicators, forwhich the test is configured. The one of the plurality of immunoassaytest pads 1704 also includes an absorbent pad 1804 for collection ofexcess biologic sample. It will be understood that the cross-sectionalview illustrated in FIG. 18 shows one well of the plurality of samplewells 1704. The other wells included in the device 1700 would beconfigured in a similar manner as that shown in FIG. 18. There may alsobe included in the device 1700 an inner separating wall between each ofthe plurality of immunoassay test pads 1704, to ensure that excessbiologic material that is not adequately absorbed by the absorbent pad1804 does not contaminate neighboring immunoassay test pads.

Referring now to FIG. 19, there is illustrated a color gradient chart1900. When the mobile application described herein captures an image ofthe testing device, in some embodiments each pixel that makes up thetest line captured in the image is processed to place it on a colorgradient scale. In some embodiments, this placement may be done byexamining the RGB values of the pixel. For any given test, there may bea visual color indicator (such as a test line) presented to the user ofthe test to indicate whether a reaction occurred. The color that is tobe presented is known for the given test. Additionally, in someembodiments, the strength of the reaction will affect the strength ofthe color indicator. For example, if a test is supposed to produce abrown colored indicator, an image can be taken of the colored indicatorto examine each pixel of the colored indicator to determine the strengthof the color produced on the testing device, which indicates thestrength of the reaction, and thus the risk level for the user.

Referring now to FIG. 20, there is illustrated a normalized past testsresults chart 2000. The captured pixels may be normalized into a singlevalue for determining whether there is a likelihood of infection,pregnancy, or whatever else the test is designed to detect. This may bedone in various ways. For example, the shade of red in all the pixelsmay be averaged to reach a single RGB value. Outliers may be left out sothat the average is not heavily skewed, especially when there are fewoutliers present. This RGB value may then be given a value, such as arisk rating, ranging from 0 to 100. For example, an RGB value of (255,255, 255) would be given a rating of 0. An RGB value of (255, 0, 0)would be given a rating of 100. An RGB value of (205, 150, 75) may begiven a rating of 70, and so on. This normalized value may then be usedto compare the user of the test to users of past tests to determine arisk level. In some embodiments, the control line and the test line maybe captured and the results compared, as well. In addition, the realresults of risk levels may also be used to adjust the stored normalizedvalue. For instance, if a particular RGB value that seems to indicate astrong reaction repeatedly was found to not indicate an infection, thisvalue may be adjusted to provide a lower risk rating. For instance, if aphysician who saw a patient who had a (205, 150, 75) RGB value laterreported to the operator of the server 1206 that further testing showedno infection was present, and if this trend continued substantially asreported by other physicians or medical organizations, subsequent testresults by other test users that were near the RGB value of (205, 150,75) may be given a lower rating.

Chart 2000 illustrates how past tests results may be collected and usedto determine the risk of a current test user. A y axis 2002 represents arisk level rating, ranging from 0 at the origin to 100. An x axis 2004represents time, wherein a plurality of normalized test results isplotted on the chart 2000. The chart 2000 is further divided intosections across the y axis 2002, indicating various risk levelthresholds. For instance, and as illustrated in the chart 2000, theremay be at certain rating levels different thresholds of risk labeled aslow, moderate, above average, and high risk levels. These thresholds maybe moved over time as more data is accumulated via users conductingtests and the mobile application storing the data on the tests. When auser conducts a test, the user's normalized rating can be plottedsimilarly to past test results and weighed against them in order toprovide a risk level for the user.

Referring now to FIG. 21, there is illustrated the mobile device 802displaying on the screen 804 a mobile application variable testfunctionality. There is displayed on the screen 804 a plurality of testfunctions 2102. The plurality of test functions 2102 may be buttons thatcan be selected by a user to switch between tests within the mobileapplication. This allows for all test functions to be within the samemobile application. For each test run by the mobile application, datafor the particular test chosen is utilized in performing the test,pulling the data from the remote server 1206.

Referring now to FIG. 22, there is illustrated the mobile device 802 ofFIG. 8B, wherein the housing 302 of the testing device 300 also includesthereon test function indicators 2202 and 2204. The test functionindicators 2202 and 2204 are visual markers located on the housing 302that identify to the mobile application the types of tests for which thetesting device 300 is configured. These indicators may be any symbol,alphanumeric character, shape, etc. that can be added to the surface ofthe testing device 300. The mobile application is programmed torecognize the indicator and perform the test function associated withthe indicator. For example, the embodiment illustrated in FIG. 22 showsa “P” symbol for test function indicator 2202 and a “Z” symbol for testfunction indicator 2204. In this embodiment, test function indicator2202 indicates that one test strip in the testing device 300 is apregnancy test, while test function indicator 2204 indicates that onetest strip in the testing device 300 is a Zika test. This is used formerely illustrative purposes, and any recognizable symbol may be usedfor these two test functions, and any other type of test may have asymbol assigned in this way as well. Further, in some embodiments theremay only be one indicator on the housing 302, even if there are multipletests. This single indicator would be for the combined test. Forexample, if the testing device 300 of FIG. 22 had a single symbol of“PZ,” this would indicate that the testing device 300 is a combinedpregnancy and Zika testing device, allowing for the mobile applicationto recognize such and perform each test with knowledge of which strip isassociated with which test based on the stored data on the testingdevice associated with the “PZ” symbol.

Referring now to FIG. 23, there is illustrated a medical codecorrelation system 2300. The system 2300 includes a mobile device 2302,which is configured to run the mobile application described herein. Themobile device 2302 is connected to a database 2304 disposed on a server2306, over a network 2308. To correlate a medical code, the mobiledevice first passes a diagnostic test identifier to the remote server.This identifier allows for the type of test being used by the user ofthe mobile device 2302 to be determined. The identifier may simply bethe name of the test, may be a number associated with the test, or anyother means of identifying the test being performed by the user of themobile device 2302. This may be done when the phone capture the image ofthe testing device to process the results, or the user may enter thetest to be performed before a test is conducted, at which time themobile device 2302 may pass the identifier to the remote server. Inother embodiments, the diagnostic test identifier may even be themedical code. For example, if the diagnostic test is a strep test, theidentifier may be G0435, an HCPCS code for a rapid immunoassay test, orany other appropriate medical code.

Once the physical test results of the diagnostic test is captured, andonce the results are processed by the mobile device 2302 or the server2306, the results are also received by the server. Once the server 2306has the diagnostic test identification and the results of the test, theserver 2306 may then correlate the specific test and the results withappropriate medical codes stored within the database 2304. It will beunderstood that the database 2304 may be physically located with theserver 2306, or the database 2306 may be a remote database, such as acentralized database that allows entities within the healthcare industryto retrieve the latest medical codes. The medical codes assigned maythen be transferred from the mobile device 2302 to a healthcare entity2310. In other embodiments, the medical codes may be transferred fromthe server 2306 to the healthcare entity 2310. The healthcare entity2310 may be a physician, a hospital, a pharmacy, an insurance company,or any other entity that may provide the user with further assistance.

Referring now to FIG. 24, there is illustrated a strep home retail testcodes table 2400. The table 2400 lists the various medical codes thatmay be associated with a home testing device that is used to test for astreptococcal infection. The table 2400 is representative of the typesof codes that may be stored in the database 2304 in relation to aparticular retail strep test device. The table 2400 lists a diagnosiscode of J02.0, an ICD-10-CM code for streptococcal pharyngitis. Thetable 2400 also lists an HCPCS code of G0435, which is a code for arapid antibody test. The table 2400 also lists an NDC code of54569-5182-0, which is the NDC code for an amoxicillin prescription.Thus, when the server 2306 assigns the medical codes to the testingdevice or the test results, various codes may be produced. The exampleshown in table 2400 shows that the strep home retail testing device isassigned the HCPCS code of G0435 to indicate the type of test it is, arapid antibody test. If the test results come back as positive, theserver assigns the ICD-10-CM code to this event, indicating astreptococcal throat infection. In response to these positive testresults, the server 2306 provides the NDC code for amoxicillin as arecommended prescription to give to the user to treat the infection.

In some embodiments, this prescription may be passed to a pharmacy sothat the pharmacy may fill the prescription for the user to pick up, orto be delivered to the user. In some embodiments, the medical codes andother information may be passed to a physician for review. Thisphysician may be the primary care physician of the user, allowing theuser to set an appointment to go over the test results and get aprescription from the physician. In other embodiments, the physician maybe a telemedicine physician that either the user contacts, the physiciansets up a telemedicine conference, or the system described hereinautomatically initiates in response to the test results. The physicianmay alter the recommended prescription provided by the system, mayconduct additional testing, or otherwise handle the situation as he orshe sees fit as a physician. In addition, in some embodiments, themedical codes may be passed to an insurance company to seekreimbursement for the testing device, the prescription, the visit withthe physician, or any other costs that arise from this testing event. Toaccomplish such, the user may have provided his or her insuranceinformation when signing up to use the mobile application in conjunctionwith the various testing devices provided.

Referring now to FIG. 25, there is illustrated a combined pregnancy andZika home retail test codes table 2500. The table 2500 lists the variousmedical codes that may be associated with a home testing device that isused to test for both pregnancy and a Zika infection. The table 2500 isrepresentative of the types of codes that may be stored in the database2304 in relation to a particular retail pregnancy/Zika testing device.The table 2500 has a pregnancy codes column and a Zika codes column. Thepregnancy codes column lists a diagnosis code of Z33.1, an ICD-10-CMcode for a pregnant state. The pregnancy codes column also lists anHCPCS code of G8802, which is a code for a pregnancy test. The pregnancycodes column also lists an NDC code of 42192-321-30, which is the NDCcode for prenatal vitamins. The Zika codes column lists a diagnosis codeof A92.5, an ICD-10-CM code for the Zika virus. The Zika codes columnalso lists an HCPCS code of G0435, which is a code for a rapid antibodytest. The Zika codes column also lists an NDC code of 50580-501-30,which is the NDC code for prescription strength Tylenol. In someembodiments, instead of separate codes for each type of test performed,there may be a single code assigned to, for example, a combinedpregnancy and Zika test, or even a single code for a pregnant with Zikainfection state.

It will be understood that the codes listed in FIGS. 24-25 are examplesof how the system may assign codes to a testing event. The use of thespecific codes and the types of codes (ICD-10-CM, HCPCS, and NDC codes)are merely used for illustrative purposes; any type of codes may be usedand the specific codes listed in FIGS. 24-25 may be other, moreappropriate, codes for the particular testing device, diagnosis, etc.Different types of codes include, but are not limited to, ICD-9-CM,ICPC-2, NANDA, Read code, SNOMED, CCI, CDT, NIC, NMDS, NOC, CCAM,OPCS-4, or other codes.

Referring now to FIG. 26, there is illustrated a flowchart of oneembodiment of a medical code correlation process 2600. The processbegins at step 2602 where the mobile device 2302 running the applicationdescribed herein transmits a diagnostic test identifier to the remoteserver 2306. At step 2604, the remote server 2306 correlates thediagnostic test identifier with a medical code associated with theparticular diagnostic test. This identifier allows for the type of testbeing used by the user of the mobile device 2302 to be determined. Theidentifier may simply be the name of the test, may be a numberassociated with the test, or any other means of identifying the testbeing performed by the user of the mobile device 2302. This may be donewhen the phone capture the image of the testing device to process theresults, or the user may enter the test to be performed before a test isconducted, at which time the mobile device 2302 may pass the identifierto the remote server. In other embodiments, the diagnostic testidentifier may even be the medical code. For example, if the diagnostictest is a strep test, the identifier may be G0435, an HCPCS code for arapid immunoassay test, or any other appropriate medical code.

The process then flows to step 2606, where the mobile device 2302, aspart of the overall operation of the system and mobile applicationdescribed herein, capture an image of the testing device for processing.At step 2608, the image is processed to achieve the test results. Suchprocessing may be performed on the mobile device 2302, on the remoteserver 2306, another server, or any other device that may be interfacedwith the system disclosed herein. The process then flows to decisionblock 2610, where it is determined whether the test results indicated apositive result (positive infection, pregnancy, or other outcomes). Ifthe test result is positive, the process flows to step 2612, where theremote server 2306 correlates a medical code with the test results. Asdescribed herein, components other than the remote server 2306 maycorrelate the test results with a medical code, such as a centralizedserver and database used in the healthcare industry to retrieve medicalcodes. The process then flows to step 2614 to assign a recommendedpharmaceutical product based on the test results. This may be an NDCcode for a product, and there may even be provided a prescription forsuch. At step 2616, the codes are transmitted to the appropriatehealthcare entities, such as a physician, a pharmacy, or other entities.

If at decision block 2610 it is determined that the test results arenegative, the process instead flows to step 2618, where no medical codeis correlated with the test results. This provides that no diagnosiscode if provided, since the test results indicate that the user is notpositive for the condition being tested. However, a medical code for thetest itself, determined at step 2604, may still be applicable in thisscenario, because the user still used the diagnostic test, and thereforemay still be reimbursed for the cost of the test, if the user'sinsurance company chooses to do so. The user's physician may also wantto see the test results, even if they are negative, and the code for thetest and the negative results may still be transmitted to the physician.Therefore, after step 2618, the process then flows to step 2616 totransmit the codes (in this case, the code for the testing deviceproduct) to the appropriate healthcare entities. The test resultsthemselves may also be transmitted.

Referring now to FIG. 27, there is illustrated one embodiment of atelemedicine initiation option within a mobile application. The mobileapplication and system described herein is predominantly meant to firsttest a patient for a medical condition, and then recommend an action,such as seeing a physician. However, the system allows for telemedicineconferences to be initiated, and therefore functionality may existwherein a user can request a telemedicine conference with a physician atany time while using the mobile application, in order to receive adiagnosis or simply to ask questions. Thus, FIG. 27 again shows theplurality of test functions 2102 displayed on the screen 804. Inaddition to these options, an additional option is presented to theuser. This option is a telemedicine conference button 2702 that allows auser to initiate a telemedicine conference with a qualified telemedicineprovider. This button 2702 is shown on the screen where the user selectsthe type of test to be performed, but the button 2702 may be presentedwithin the user interface on any screen of the mobile application.

Referring now to FIG. 28, there is illustrated another embodiment of atelemedicine initiation option within a mobile application. There isshown on the screen 804 the test results as previously shown on FIG. 22,which indicate positive pregnancy and Zika test results. In such asituation where the test results indicate a possible serious medicalcondition, a button 2802 may appear to the user on the screen 804. Thebutton 2802 may have a warning message within, urging the user to seek aconsultation with a physician to talk about the user's options in lightof the positive test results, and inviting the user to tap the button2802 to initiate a telemedicine conference with a physician. Tapping thebutton 2802 will initiate such a telemedicine conference. The physicianthat is connected with for the conference may be an on-call physicianthat has agreed to make his or her services available through the systemdescribed herein, the telemedicine conference may use an existedtelemedicine conference platform and physician base, the user's primarycare physician may be a user of the system and mobile application,allowing for them to be used as the default telemedicine contact for theuser, or other methods of making physicians available for a conferencewith the users of the mobile application may be provided.

Referring now to FIG. 29, there is illustrated one embodiment of atelemedicine conference session on a mobile device. During atelemedicine conference that has been initiated as described herein, theuser is presented with a video conference window 2902 on the screen 804.The video conference window 2902 allows for user to see the physicianthat is providing the telemedicine services to the user. It will beunderstood that the physician may have a similar video window on thedevice being used by the physician that allows the physician to see theuser. This allows the physician to make some visual observations of theuser's condition. In addition to the video conference window 2902, theuser is presented with a plurality of actions 2904 on the screen 804.The plurality of action 2904 may be buttons that allow the user toprovide the physician with further information. For example, one buttonmay allow for the user to send a photograph to the physician, such as aphotograph of the user's symptoms, or of the user's test resultspresented on the testing device. One button may also provide an optionfor sending the user's medical file to the physician, so that thephysician can review the user's medical history or other importantinformation. This medical file may include all the informationaccumulated from all tests performed by the user under the systemdescribed herein, and may also include all other medical historyinformation. The user may have provided a copy of his or her medicalhistory, or such may have been retrieved from a central electronicmedical records system.

Other actions that may be provided in the plurality of actions 2904 maybe a button to send test results to the physician. This would allow theuser to send the test results of the latest test the user took beforeinitiating the telemedicine conference, or it may allow for the user tochoose the test. The plurality of actions 2904 may also include a buttonfor sending the user's insurance information to the physician. The usermay have provided this information within the mobile application and hadit stored to the server, or this information may have been pulled via aconfidential link from a centralized database for the user based on theuser's identification information. This option allows the user to givethe physician insurance information so that the physician can use theuser's insurance for reimbursement of the telemedicine services, and mayeven set up reimbursement to the user for certain services or products,such as the testing device used for the test.

Referring now to FIG. 30, there is illustrated a flowchart of oneembodiment of a medical file handoff process 3000. The process 3000starts at step 3002 where a user is provided with diagnostic testresults at the conclusion of a performance of a test. At decision block3004, it is determined whether the test results provide a positiveresult. If not, at step 3006 the results are stored on the server of thesystem described herein and the process ends at end block 3016. If theresults are positive, the process flows to step 3008 where the resultsare stored on the server. At step 3010, it is determined whether atelemedicine conference has been initiated. This initiation may havebeen selected as described with respect to FIGS. 27 and 28, may havebeen initiated automatically due to the results provided, or may havebeen initiated in some other way. If the telemedicine conference was notinitiated, the process ends at end block 3016. If the telemedicineconference was initiated, the process flows to step 3012 where the testresults are passed to the telemedicine provider participating in thetelemedicine conference. The process then flows to step 3014, whereother user information is passed to the telemedicine provider. Theprocess then ends at end block 3016.

The passing of the results to the telemedicine provider and otherinformation at steps 3012 and 3014 may be performed by the user's mobiledevice, wherein the mobile device sends the files to the telemedicineprovider. The passing may also be done by the server of the systemdescribed herein, wherein the results and other information werepreviously stored to the server and the server then passes the resultsand other information to the telemedicine provider as a result of theserver being notified of a telemedicine conference initiation. The otheruser information of step 3014 may be any information needed by thetelemedicine provider, such as past medical records and medical historyof the user, past test results, insurance information, or any otherinformation.

Referring now to FIG. 31, there is illustrated a flowchart of oneembodiment of a telemedicine conference initiation process 3100. Theprocess 3100 starts at step 3102 where a user is provided withdiagnostic test results at the conclusion of a performance of a test. Atdecision block 3104, it is determined whether the test results provide apositive result. If not, at step 3106 the results are stored on theserver of the system described herein and the process ends at end block3118. If the results are positive, the process flows to step 3108 wherethe results are stored on the server. At step 3110 a telemedicine buttonis presented to the user on the screen of the mobile device, similar tothat shown in FIG. 28. This button recommends to the user that the userinitiate a telemedicine conference, since the test results indicate apositive reaction. At step 3112, it is determined whether a telemedicineconference has been initiated. This initiation may have been selected asdescribed with respect to FIGS. 27 and 28, may have been initiatedautomatically due to the results provided, or may have been initiated insome other way. If the telemedicine conference was not initiated, theprocess ends at end block 3118. If the telemedicine conference wasinitiated, the process flows to step 3114 where the test results arepassed to the telemedicine provider participating in the telemedicineconference. The process then flows to step 3116, where other userinformation is passed to the telemedicine provider. The process thenends at end block 3118.

The passing of the results to the telemedicine provider and otherinformation at steps 3114 and 3116 may be performed by the user's mobiledevice, wherein the mobile device sends the files to the telemedicineprovider. The passing may also be done by the server of the systemdescribed herein, wherein the results and other information waspreviously stored to the server and the server then passes the resultsand other information to the telemedicine provider as a result of theserver being notified of a telemedicine conference initiation. The otheruser information of step 3116 may be any information needed by thetelemedicine provider, such as past medical records and history of theuser, past test results, insurance information, or any otherinformation.

Turning now to FIG. 32A, there is illustrated an embodiment of a systemin which a prescription is transmitted to a pharmacy using aself-diagnostic test and telemedicine. In these embodiments, rather thanthe patient needing to physically travel to a pharmacy to drop off aprescription to be filled, the user uses a mobile application toelectronically transmit the prescription information to the pharmacy.These embodiments improve upon embodiments which use self-diagnostictests and telemedicine and take advantage of the fact that the user isalready engaged in a telemedicine session with the user's healthcareprovider through a network 3202 such as the internet. In theseembodiments, the user engages in a telemedicine session with ahealthcare provider as described hereinabove, via Path {circle around(1)}. When the user and the healthcare provider complete thetelemedicine session, the healthcare provider can prescribe necessarymedicine to the mobile application user. However, since the user is notphysically present with the healthcare provider, the user does not pickup a physical prescription slip. Instead, the healthcare providertransmits via Path {circle around (2)} the prescription in electronicform either to the user's mobile application, or to the pharmacy of theuser's choice. If the healthcare provider transmits the “electronicprescription” to the user's mobile application, then the user can thenstore the electronic prescription on his mobile device 802 in the mobileapplication until he is ready to get the prescription filled. The userthen uses the mobile application to send the electronic prescription tothe pharmacy via Path {circle around (3)}. The pharmacy then fills theprescription as normal.

Turning now to FIG. 32B, there is illustrated another embodiment of asystem in which a prescription is transmitted to a pharmacy using aself-diagnostic test and telemedicine. These embodiments are similar tothose described hereinabove with respect to FIG. 32A. The systemincludes a user with a mobile device 802 running a mobile application, ahealthcare provider, a pharmacy, and a remote server or central officewith a records database. In these embodiments, the user participates ina telemedicine session with a healthcare provider via Path {circlearound (1)} as described hereinabove with respect to FIGS. 29-31. Next,if the healthcare provider decides that a prescription is needed, thehealthcare provider creates a prescription record and transmits therecord through a network 3202 such as the internet to a central office3204 or remote server via Path {circle around (2)}. The central office3204 then stores the record in a records database 3206. When the user isready to have their prescription filled, they use the mobile applicationon the mobile device 802 to contact the central office 3204 via Path{circle around (3)}. The central office 3204 then retrieves theprescription record from the database 3206 and sends the prescriptionrecord to the pharmacy via Path {circle around (4)} to have theprescription filled. With this method, the healthcare provider does nothave to worry about which pharmacy to send the prescription to, and thefact that the prescription record does not have to be stored on themobile device 802 means that the user could potentially access theprescription record from another mobile device or any other compatibledevice with network access.

Turning now to FIG. 33, there is illustrated an embodiment in which themobile application running on the mobile device 802 displays whatprescriptions have been prescribed by the healthcare provider to theuser. In these embodiments, the mobile application informs the user whatprescriptions have been issued or “written” for him by the healthcareprovider without the need of physical records. The user receives anotification from the mobile application when the healthcare providerhas given the prescription. For example, if the healthcare providerissues (“writes”) the prescription during the telemedicine session, thescreen illustrated in FIG. 33 will be presented at that time. Or, if thehealthcare provider writes the prescription after the telemedicinesession has ended, the user will be notified by the mobile applicationat that time.

Turning now to FIG. 34, there is illustrated a mobile device 802 from anembodiment in which the user can select which pharmacy to send theprescription to. In these embodiments, a menu displays a choice ofpharmacies. These choices can be based on geographic location, on whichpharmacies accept the user's insurance, or any other factor which mightinfluence a user's choice of pharmacy. Once the user selects whichpharmacy will fill the prescription, the prescription record istransmitted to that pharmacy so that it can be filled. In someembodiments, a preferred pharmacy is selected ahead of time, so that theuser does not have to select a pharmacy each time the user receives aprescription from a healthcare provider. In these embodiments, the useris presented instead with a confirmation screen which user will use tosend the prescription to the previously-chosen pharmacy to be filled.

Turning now to FIG. 35, there is illustrated a mobile device 802 from anembodiment of the system which allows for the prescription to either bepicked up or delivered. In some embodiments of the system, the user isoffered the convenience of having the prescription delivered to theuser's home or place of work. In these embodiments, when a prescriptionis sent to a pharmacy to be filled, the user is presented with a menu inthe mobile application which gives him the option of choosing to pick upthe prescription himself, or of having the prescription delivered. Ifthe user selects to have the prescription delivered, the user will thenbe presented with a screen in the mobile application where he or sheenters the delivery address. Some embodiments will allow for addressesto be pre-entered into the mobile application and saved. This will speedup future prescription fillings, as the user will not have to enter thedelivery address every time he selects to have a prescription delivered.In some embodiments, if the user selects to pick up the prescription,the user will be given an estimated ready time for the prescription or anotification through the mobile application when the prescription isready to be picked up.

Turning now to FIG. 36, there is illustrated a flowchart of the processfor using a self-diagnostic test and telemedicine to obtain aprescription. The process starts at Start block 3602 and proceeds toblock 3604. At block 3604, the user performs a self-diagnostic test suchas is described hereinabove with respect to FIGS. 9 and 11. Next, atblock 3606, a telemedicine session is established and occurs between theuser and a healthcare provider as described hereinabove with respect toFIGS. 29-31. Next, the process moves to block 3608, where the healthcareprovider determines that the user needs a prescription. In someembodiments, this step takes place during the telemedicine session.Next, the process moves to block 3610, where the healthcare providerissues a prescription for the user and enters the prescriptioninformation into the telemedicine system. Next, at block 3612, the useris notified through the mobile application that they have beenprescribed medication. The process then moves to block 3614, where theuser selects a pharmacy to fill the prescription. As discussedhereinabove with respect to FIG. 34, this step may not take place if theuser has a pharmacy pre-selected. Next, at block 3616, the mobileapplication causes the prescription to be sent to the pharmacy to befilled. The process then moves to block 3618, where the pharmacy fillsthe prescription. The block then moves to decision block 3620, where theuser chooses whether the prescription will be picked up or delivered. Ifthe user chooses to pick up the prescription, the process moves tofunction block 3622, where the system sends the user a notification thatthe prescription is ready for pick-up. The process moves to block 3624,where the user picks up the prescription and then ends at block 3626. Ifthe user chooses to have the prescription delivered, then the processmoves to block 3628, where the prescription is delivered to the user athis selected address. The process then ends at block 3626.

Turning now to FIG. 37, there is illustrated an embodiment in which atelemedicine mobile application is used to automatically fill aprescription. In some cases, when a patient is diagnosed with aparticular ailment, the prescription is likely to be a predeterminedmedication or set of medications. In these cases, a healthcare providercan often issue a prescription for the user without having to actuallysee or talk to the user. Having a user's health history and the resultsof a diagnostic test are often enough for a healthcare provider to issuea prescription for a user. Some embodiments take advantage of thesesituations and improve the efficiency of the telemedicine andprescription-filling process by allowing prescriptions to be issued andfilled automatically, without significant interaction between the userand the healthcare provider. The process starts at Start block 3702 andproceeds to function block 3704, where the user performs aself-diagnostic test. The process then moves to decision block 3706. Ifthe self-diagnostic test returns negative results, the process loopsback to block 3704 until the user performs another self-diagnostic testsometime in the future. If the test results are positive, the processmoves to decision block 3708. If the positive result from the test doesnot indicate a “critical” or urgent situation, the process movies toblock 3710, where a normal telemedicine proceeding occurs, as describedhereinabove with respect to FIGS. 27-31. If, however, the resultsindicate an urgent or critical situation which can be resolved withoutsignificant user interaction with a healthcare provider, the processmoves to function block 3712. At block 3712, the mobile applicationtransmits the self-diagnostic test results to a central office or remoteserver for the telemedicine system. The process moves to block 3714,where a healthcare provider is assigned to the user's test results,which are transmitted by the central office to the healthcare provider.The process then moves to decision block 3716, where, if the user haspre-registered, that is, has supplied their health history and pharmacypreferences to the telemedicine system, the process moves to block 3722,where the healthcare provider compares the user's health history withthe self-diagnostic test results to determine if a prescription should(can) be issued to the user.

If, at block 3716, the user has not pre-registered, the process moves toblock 3718, where a session of the telemedicine application is opened onthe user's mobile device. This session is simply for the user to providethe information necessary for the healthcare provider to issue theproper prescription. The process moves to block 3720, where the userprovides their health history and their pharmacy preferences to thetelemedicine system through the mobile application. Next, the processmove to block 3722, where the healthcare provider compares the user'shealth history and the test results to determine if a prescriptionshould be issued. The process then moves to block 3724, where thehealthcare provider issues a prescription and sends it to the pharmacy.The process moves next to block 3726, where the telemedicine applicationopens on the user's mobile device. At block 3728, the telemedicinemobile application informs the user that the prescription has beenfilled by the pharmacy and is ready for pick-up or delivery. The processthen ends at End block 3730.

Turning now to FIG. 38, there is illustrated a mobile device 802 from anembodiment of the system in which the user obtains a real-time healthinsurance quote in response to a self-diagnostic test. These embodimentsallow users to obtain health insurance quotes from multiple insuranceproviders or for multiple different plans through the mobile applicationrunning on the mobile device 802. Rather than having to call or researchdifferent insurance providers individually, a user can utilize thesystem to obtain multiple quotes from multiple providers in a relativelyshort period of time. This provides a significant advantage to the userby letting them compare multiple insurance plans quickly andconveniently. An information input menu 3802 is presented to the user bythe mobile application. The user inputs information relevant toobtaining an insurance quote, such as age, gender, income level (toaccount for possible government subsidies), or other health-relatedinformation. In some embodiments, the information input menu also allowsthe user to input information about their current insurance, such astheir current insurance provider or insurance plan number. This allowsthe system to provide quotes for insurance plans which provide similarbenefits to the user's current plan. Once the user enters the necessaryinformation into the information input menu 3802, the information istransmitted to a central office, which then queries participatinginsurance carriers for insurance premium quotes which are then presentedto the user.

In some embodiments, the query for a real-time insurance quote occursafter a user has conducted a self-diagnostic test. In these embodiments,the information transmitted to the central office by the mobileapplication includes what type of test the user conducted, and in someembodiments, the results of the test. This allows the insurance carriersto provide more accurate insurance quotes to the user.

In some embodiments, the real-time insurance quotes are for healthinsurance. In other embodiments, the quotes are for life insurance. Instill other embodiments, the quotes are for disability insurance. Someembodiments provide quotes for multiple types of insurance.

Turning now to FIG. 39, there is illustrated an embodiment of the systemin which multiple insurance plans are presented through a mobileapplication to a user. In these embodiments, after the user submitsinformation as described hereinabove with respect to FIG. 38, a centraloffice returns to the mobile device 802 quotes for one or more healthinsurance plans provided by the queried insurance providers. The mobileapplication then presents the quotes to the user in a quote menu 3902.These quotes are based on the information supplied by the user, and insome cases, on information regarding the type or results of the testconducted by the user. The quote menu 3902 displays not only theinsurance quotes, but also basic information about the insurance quotes,such as the insurance provider or premium cost for each respectivequote. In some embodiments, the quote menu 3902 includes “buttons” 3904for each quote which the user can click. Clicking a “button” 3904 causesthe mobile application to present a new screen, described herein belowwith respect to FIG. 40, which gives more detailed information about thequote which the user touched.

Turning now to FIG. 40, there is illustrated an embodiment of the systemin which more detailed information regarding a health insurance quote ispresented to a user. In these embodiments, when a user clicks on abutton related to a particular insurance quote, as described hereinabovewith respect to FIG. 39, the mobile application presents the user withan insurance plan details screen 4002. The insurance plan details screen4002 presents the user with additional details about the insurance planchosen by the user to look at. This information can include the monthlyor yearly premium, any deductible, any out-of-pocket maximum, what typesof services are and are not covered, any government subsidies that mightbe available to a user who selects that plan, or any other piece ofinformation that would be useful to a user contemplating selecting thatinsurance plan. The plan details screen 4002 may also include a websiteor phone number where a user can obtain additional information about theplan or the insurance provider. In some embodiments, the plan detailsscreen 4002 will also have a banner or button 4004 which the user canclick to be connected with a representative from the selected insurancecompany who can provide more information to the user or assist the userin signing up for the insurance plan. Different embodiments will allow auser to connect to an insurance provider representative in differentways. For example, some embodiments will provide a text chat between theuser and the representative. Other embodiments will provide two-wayaudio or video connections between the user and a representative thoughwhich the user can discuss the insurance plans or even sign up for aplan.

Turning now to FIG. 41, there is illustrated a diagrammatic view of asystem for providing real-time health insurance quotes in response to aself-diagnostic test. In these embodiments, a system includes a user4102 using a mobile device 802 which runs a mobile application. After auser conducts a self-diagnostic test and enters any required informationinto the mobile application, the mobile device 802 transmits theinformation (which might include information about the self-diagnostictest or its results) via Path {circle around (1)} through a network4104, such as the internet, to a central office 4106. The central office4106 keeps a database of records which include any insurance providersparticipating in the real-time insurance quote system. The centraloffice 4106 transmits the user information via Paths {circle around (2)}to various participating insurance providers 4108. Each insuranceprovider 4108 then uses the information provided by the central office4108 to generate a quote (or to decline generating a quote). Theinsurance providers 4108 the return insurance quotes to the centraloffice 4106 via Paths {circle around (3)}. Finally, the central office4106 communicates the insurance quotes via Path {circle around (4)} tothe mobile application running on the mobile device 802 for the user toview.

Turning now to FIG. 42, there is illustrated a flowchart depicting aprocess for generating a real-time health insurance quote in response toa self-diagnostic test. The process starts at Start block 4202 and movesto function block 4204, where the user performs a self-diagnostic testwith a mobile application, such as is described hereinabove with respectto FIGS. 8-11. Once the user has completed he self-diagnostic test, theprocess moves to block 4206, where the mobile application presents theuser with a menu which allows the user to enter additional informationrelevant to an insurance quote. The user enters any additionalinformation requested, and the process moves to block 4208. At block4208, the mobile application transmits the user information (and, insome embodiments, test-related information) to a central office througha network such as the internet and requests quotes for health insurancecoverage. The process moves next to block 4210, where the central officeaccesses a list of participating insurance providers who accept quoterequests through the system. The process moves to block 4212, where thecentral office transmits requests for quotes, along with the supplieduser information, to the participating insurance providers. At block4214, the each participating insurance provider uses the informationreceived from the central office to generate personalized insurancequotes for the user. In some cases, instead of generating an insurancequote, an insurance provider may determine that, based on the userinformation provided, it cannot provide insurance coverage to the user,and thus will not return an insurance quote. The process moves to block4216, where the insurance providers transmit the quotes (or a messageindicating that it will not insure the user) to the central office.Next, at block 4218. The central office transmits the received insurancequotes to the user and the mobile device 802. Next, at block 4220, themobile device 802 and mobile application present the received insurancequotes to the user. In some embodiments, the process will end at block4220. In other embodiments, the process will move to block 4222, wherethe mobile application allows the user to view details of one or more ofthe insurance plans quoted to the user. The user selects a quote theyare interested in, and the mobile application presents details aboutthat plan. The process then moves to block 4224, where the mobileapplication presents the user with options for contacting the insuranceprovider offering the selected insurance plan, such as a phone number,email, or website. In some embodiments, the mobile application willoffer the option of connecting the user to an insurance providerrepresentative through the mobile application, such as through a textchat or a two-way audio or video connection. The process then ends atEnd block 4226.

The images of the testing device 302 (FIG. 3) that have been capturedmay be improved by processing devices using various image processingtechniques in order to determine the test results indicated within theviewing windows 306. In addition to the testing devices 302 describedhereinabove other types of images may be taken of viewing windows suchas those described in U.S. Provisional Patent Application No.62/584,704, entitled ARTIFICIAL INTELLIGENCE RESPONSE SYSTEM BASED ONTESTING WITH PARALLEL/SERIAL DUAL MICROFLUIDIC CHIP, filed on Nov. 10,2017 (Atty. Dkt. No. RIDL60-33874), which is incorporated herein byreference in its entirety. Additional types of viewing windows which maybe imaged processed are described herein below.

Referring now to FIG. 43, there is illustrated a side cross-sectionalview of an RT-lamp. The RT-lamp is a Reverse Transcription Loop-mediatedisothermal Amplification device, which is a technique for theamplification of RNA. This combines the advantages of the reversetranscription with the LAMP technique. The LAMP technique is a singletechnique for the application of DNA. This technique is an isothermalnucleic acid application technique, in which a chain reaction is carriedout at a constant temperature and does not require a thermal cycler. Thetarget sequences are modified at a constant temperature using either twoor three sets of primers and polymerase with high strand displacementactivity in addition to a replication activity. The addition of thereverse transcription phase allows for the detection of RNA and providesa one-step nucleic acid amplification method that is used to diagnoseinfectious diseases caused by bacteria or viruses.

FIG. 43 illustrates an example in which a multimode instrument 4301 iscoupled to a smartphone 4302. The smartphone 4302 includes an LED 4304and a camera 4306. The camera 4306 includes an image sensor, such as aCCD (charge coupled device). The instrument 4300 includes a samplechamber 4308 for receiving an optical assay medium. The optical assaymedium comprises the labeled biologic sample disposed within the viewingwindow on the microfluidic chip. The sample chamber 4308 may include adoor 4310 that prevents stray light from entering.

The optical assay medium is positioned over a detection head 4312 in thesample chamber 4308. The instrument 4300 includes an optical output pathfor receiving an optical output from the optical assay medium in thesample chamber 4308 via the detection head 4312. The optical output pathmay include a multimode fiber 4314 that directs light from the detectionhead 4312 to a cylindrical lens 4316. The optical output path mayfurther include a wavelength-dispersive element, such as a diffractiongrating 4318, that is configured to disperse the optical output intospatially-separated wavelength components. The optical output path mayalso include other optical components, such as collimating lenses,filters, and polarizers.

The instrument 4301 can include a mount for removably mounting thesmartphone 4302 in a working position such that the camera 4306 isoptically coupled to the optical path, for example, in a predeterminedposition relative to the diffraction grating 4318. In this workingposition, the camera 4306 can receive at least a portion of thedispersed optical output such that different locations are received atdifferent locations on the image sensor.

The instrument 4301 may also include an input optical path for directinglight from a light source to the optical assay medium in the samplechamber 4308, for example, through the detection head 4312. In someinstances, the LED 4304 on the smartphone 4302 could be used as thelight source. To use the LED 4304 as the light source, the input opticalpath may include a collimating lens 4320 that receives light from theLED 4304 when the smartphone 4302 is mounted to the instrument 4300 inthe working position. The input optical path may further include amultimode fiber 4322 that directs the light from the collimating lens4320 to the detection head 4312. The input optical path may also includeother optical components, such as collimating lenses, filters, andpolarizers.

The instrument 4300 may also include an additional input optical paththat directs light form an internal light source, such as a laser 4324,to the optical assay medium in the sample chamber 4308. The additionalinput optical path may include a multimode optical fiber 4326, as wellas collimating lenses, filters, polarizers, or other optical components.

Referring now to FIG. 44, there is illustrated the view of the RT-lamp4301 with a microfluidics chip 4402 disposed within the sample chamber4308.

Referring now to FIG. 45, there is illustrated a side view of the smartphone 4302 interfaced with the microfluidic chip 4402 for imaging thesurface thereof, which is illustrated in a window view in FIG. 46. Thiswindow view illustrates the viewing window as a box 4602 in which theimage of the microfluidic chip 4402 is displayed. The applicationautomatically recognizes various markers 4604, 4606 and 4608 on threecorners thereof. This will allow orientation of the window with respectto the application. A box 4610 in phantom dashes will be oriented by theapplication running on the smart phone 4302. Once the box has beenoriented visually about the image of the microfluidic chip 4402, thenprocessing can proceed. The processing is basically focusing upon thechip to gain the best optical image of the target sites. The targetsites are storage reservoirs 4612 and 4630, for example. Each of thesewill have a viewing well 4618 associated therewith, and these viewingwells 4618 will have, in one example, a process biologic sample havingaffinity labels associated there with that fluoresce. By recognizing theflorescence, the presence of the cell can be determined. The lack offlorescence indicates that the cell, a bacterium for example, has beendestroyed. This can be a positive test. By examining at each stage ofthe testing process the chip, a determination can be made as to resultsin essentially real time. This will be described in more detail hereinbelow. Once the image is believed to be in focus, the user can actuallytake the picture or the application cell can automatically determinethat the focus is adequate and take that. This is very similar tocharacter recognition techniques that are utilized in recognizing facesin camera images received by the phone.

FIG. 47 illustrates a diagrammatic view of a bio-fluidic analysis system4700 in accordance with various embodiments of the present disclosure.The system 4700 may include a mobile device 4702. The mobile device 4702may be a mobile handheld user device, such as a smart phone, tablet, orthe like. The mobile device 4702 may include a processor 4704, a memory4706, an input/output (I/O) interface 4708, a display 4710, and acommunication interface 4712 all connected via a bus 4714. Thecommunication interface may connect the mobile device 4702 to outsidesources, such as a server 4716 having a database 4718 associatedtherewith, over a network 4720, i.e. a cellular network or Internetnetwork. The memory 4706 may store an operating system 4722 and variousspecial-purpose applications, such as a browser by which webpages andadvertisements are presented, or special-purpose native applications,such as weather applications, games, social-networking applications,shopping applications, and the like. The digital data package mayprovide data to a special purpose native application 4724 stored in thememory 4706, the application 4724 having associated therewith anapplication programming interface (API) 4726. The digital data packagemay be obtained by the mobile device 4702 by a test results capturemodule 4728 connected to the processor 4704. The test results capturemodule 4728 may capture an image, scan, video, or other digital media ofa testing device 4730, converting the analog biologic sample testingdevice and the results presented on the device to a digital format andto create a unique identifier that can be used to trigger a plurality ofevents. The test results capture module 4730 (or an associated module)may also perform image processing on the captured image so that the testresults may be better analyzed.

The unique identifier comprising the digital data package may beanalyzed by the application 4724 to determine the results from theanalog testing device. In some embodiments, the determination of thetest results, due to the type of analog testing device, is notdetermined locally by the application 4724. In some embodiments, theunique identifier may be transmitted to the server 4716, via the network4720, for remote analysis of the data contained in the uniqueidentifier. In some cases, results from the analog testing device may bedetermined locally and remotely. In some instances, the user of themobile device 4702 may not have cellular network or Internet connection,for instance, the settings for connectivity on the mobile device 4702 isdisabled, turned off or a combination thereof. In this case, thetransmission of the unique identifier to the server 4716 may bepostponed until a connection is available.

In some embodiments, the mobile device 4702 may include a locationsensor, such as a global positioning system (GPS) sensor or othercomponents by which geographic location is obtained, for instance, basedon the current wireless environment of the mobile device 4702, likeSSIDs of nearby wireless base stations, or identifiers of cellulartowers in range. In some cases, geographic locations are inferred by,for instance, an IP address through which a given mobile device 4702communicates via the Internet, which may be a less accurate measure thanGPS-determined locations. In other cases, geographic location isdetermined based on a cell tower to which a mobile device 4702 iswirelessly connected. Depending on how the geographic data is acquiredand subsequently processed, that data may have better or less reliablequality and accuracy.

FIG. 48 illustrates a diagrammatic view of an analog testing device to adigital format and unique identifier conversion process 4800 inaccordance with various embodiments of the present disclosure. A testingdevice 4802 may provide medical test results in an analog format, suchas in a results display window 4804 indicating a positive or negativesign, a color spectrum, a line, a circle, a curve, a balloon, asignature marker, or variance of the like. A biologic specimen may bedeposited into the testing device 4802 where the biologic may bind orreact with particular reagents specific to the type of test to which thetesting device 4802 pertains. The testing device 4802 may also include atest type identifier 4806, such as a code, graphic, symbol, or otherindicator on a surface of the testing device 4802.

A mobile device 4808, which may be the mobile device 4702 describedherein, may include a capture device 4810. The mobile device 4808 mayconvert use the capture device 4810, in addition to other data known orotherwise obtained by the mobile device 4808, to convert the analog dataand biologic presented by the testing device 4802 to a digital uniqueidentifier. When digital media such as an image, video, or other digitalformat of the testing device 4802 is captured by the capture device4810, certain properties may be analyzed, processed, and stored into asa digital data package. For instance, the test type associated with thetesting device 4802 may be determined by the mobile device 4808 byidentifying the particular test associated with the test type identifier4806 captured within the digital media.

Test results provided in the results display window 4804 or elsewhere onthe testing device 4802 may also be captured within the digital mediaand analyzed. For example, in the case of a color indicator as theresult of the test, the RGB values of the pixels contained in thedigital media of the test results may be determined in order to providea digital value for the test results. The test result may be stored inthe digital data package in a particular digital format, for instance, apositive or negative test result value. The value may be a binary value,a rating, a probability, or other type of result indicator. The biologicspecimen used to conduct the test may also be included in the digitaldata package. The biologic specimen provided into the testing device4802 may be determined from the test type identifier 4806, since in manycases the specific test will dictate the biologic to be used.

The data provided by the digital data package may also include the type,manufacture and serial number of the testing device 4802, and atimestamp for when the capture device 4810 captured the digital media.The manufacture, serial number and cellular provider of the mobiledevice 4808 may also be included in the digital data package. Theapplication 4724 may then generate the unique identifier 4812 from thedata of the testing device 4802 and mobile device 4808, in combinationwith data of the user of the mobile device 4808. Data of the user may bethe user's name, birthday, age, gender, social security number, height,weight, race, diagnosis status, insurance information, medical codes,drug codes, and the like, and a combination thereof.

In some embodiments, the unique identifier may be verified by averification server, such as the server 4716, to determine theauthentication of the biological specimen. In some cases, the user mayprovide the analog testing device 4802 with a substance not classifiedas a biological specimen. In this instance, an application on the server4716 will provide the application program 4724 with a message indicatingan error, in which the user may be required to provide a biologicalspecimen to a different analog testing device. In some embodiments,after verification of a biological specimen, the local applicationprogram 4724 or the server 4716 via the user's application program 4724will provide the user with a positive or negative outcome of the analogtesting device 4802. In some cases, the user is displayed a negativetest result and the application program 4724 of the mobile device 4808indicates that testing is completed. In other cases, the user isdisplayed a positive test result by the application program 4724 on thedisplay 3110 of the mobile device 4808.

The unique identifier 4812 may include a plurality of digital datastreams 4814 used during creation of the unique identifier 4812, such asinformation included within the digital data package, or otherwise knownor obtained by the mobile device 4808 or the server 4716. The pluralityof digital data streams 4814 (D1, D2, D3, D4 . . . Dn) may be assembledtogether to create the unique identifier 4812, and the mobile device4808, the server 4716, or the authorized system components may parse ordeconstruct the unique identifier 4812 to analyze specific userproperties or test properties, and to trigger events based on theproperties.

Creating a single unique identifier 4812 which contains many differentitems of information is an efficient way of associating many differenttypes of information with a single biologic, user, test, etc. Every timea test is conducted, a new unique identifier 4812 may be created. Eachunique identifier created may include the plurality of data streams4814. Each one of the plurality of data streams 4814 in the uniqueidentifier 4812 stores a different type of information. In someembodiments, the information stored in data streams 4814 includes thetest type, the test results, demographics of the user, or anidentification number, such as an IMSI number, for the mobile device4808. In some embodiments, the unique identifier 4812 is set up in astructural format, such that each data stream 4814 is a subcomponent ofthe unique identifier 4812. In some embodiments, unique identifier 4812is a string of alphanumeric characters, and the data streams 4814 whichmake up the unique identifier 4812 are simply different portions of thecharacter string. In these embodiments, the format of the uniqueidentifier 4812 is known to a database or server which can correctlyparse the unique identifier 4812 into the separate data streams 4814 foranalysis.

Referring now to FIG. 49, analyzing medical self-tests from a computervision aspect typically such as those describe above involves three mainsteps. In real time, a determination is made at step 4902 if the testdevice is in the picture. A medical self-test would comprise any medicaltest executed by an individual that was not executed in a lab orprofessional environment. For example, but not limited to, a personperforming a flu test on themselves or a family member. If the testdevice is located in the picture, a photo of the test device isautomatically taken at step 4904 (a.k.a. auto capture). If the testdevice is not located within the picture, the process is completed andno picture is taken. Next, the area of the test device that contains thediagnostic information (e.g., the test strip embedded in the testcassette) is identified at step 4906. The diagnostic information (e.g.,one or more test lines within the test strip window of a cassette) areread and classified at step 4908.

The algorithms that perform these steps should be invariant to thefollowing so-called perturbations:

Any variation in the location of the test device within the image

Any variation of the orientation of the test device

Any variation in color of the test device

Any variation in color sensitivity of the camera

Variable lighting conditions

Shadows

Glare

Dirt on the test devices

Noise

The main challenge faced when developing robust computer visionalgorithms for automatically reading and classifying a medicalself-test, is to design algorithms that are insensitive to the abovelisted perturbations.

To design computer vision algorithms resilient to the perturbationslisted above, it is important to perform the image processing based ongeometrical attributes of the image, rather than (color) intensityattributes. This is typically achieved by using some form of edgedetection to generate a description of different features of the testdevice. However, traditional edge detection methods suffer from severaldrawbacks that make such algorithms unreliable when faced with some ofthe perturbations listed above. Such drawbacks include:

-   -   Sensitivity to noise, particularly under low lighting        conditions.    -   Use of de-noising methods often tends to give poor localizations        of edges.    -   The edge detection response does not always correspond to the        geometrical fidelity of the edge, but rather to the color        intensity.    -   Poor accuracy for detecting thin lines.    -   Edge detection methods tend to pick up all edges in an image,        including edges that are not useful for the feature detection        task (over-segmentation).    -   Edge detection methods by themselves are not useful for        measuring the curvature of an edge.    -   The methods typically require a large number of parameters to be        tuned for good performance.

In order to overcome these problems, a generalization of the Curve FieldTransform (CFT) (a.k.a. the Curve Filter Transform) may be used. Usingthis method has several advantages compared to traditional (edge)segmentation methods, including:

-   -   Resilience to noise and low contrast.    -   The CFT response corresponds to the geometrical fidelity of the        edge, rather than the color contrast of the edge.    -   Works well for thin features.    -   Can classify edges and curves according to their curvature,        angle, and scale.    -   Only requires one basic parameter, the scale(s) of the        feature(s) to be detected.

The Generalized Curve Field Transform (GCFT)

Referring now to FIG. 51, the main ideas of the CFT (Curve FieldTransform) can be summarized as follows. The bottom portion of FIG. 50shows the orientation fields for the region in the upper right image.The darkness of each line segment indicates the reliability of thecorresponding orientation. Next, compute the orientation based onintegration, rather than differentiation at step 5102. Using integrationmakes the method significantly more resilient to noise. The assignmentof an orientation to each pixel results in an orientation field. Theprocess of computing the orientation at step 502 includes the steps ofcomputing the reliability weight at step 5104 such that it measures thegeometrical fidelity of the underlying structure, rather than the(local) contrast of the underlying structure and computing anorientation angle at step 5106. A notion of “backward/forward” is notassociated with the line segment, as is traditionally done for gradientvectors of an image. This means that the orientation angle has aperiodicity of 180 degrees rather than 360 degrees. A directed linesegment (orientation) is assigned at step 5108 consisting of an angleand a reliability value based upon the computed orientation andreliability. The reliability comprises weight assigned to each pixel inthe image (see FIG. 50). Lines and curves are located at step 5110 usingalignment integrals in the orientation field along which the underlyingorientations align in a consistent manner as shown in FIG. 52. Thesuperimposed thick curve 5202 of the image illustrates a line alongwhich the underlying orientations align with the superimposed curve.

A general set of references on the Curve Field Transform, and therelated Orientation Field Transform are described in [1] Sandberg, K.,Brega, M.: Segmentation of thin structures in electron micrographs usingorientation Fields. J. Struct. Biol. 157, 403-415 (2007); [2] Sandberg,K.: Methods for image segmentation in cellular tomography. In: McIntosh,J. R. (ed.), Methods in Cell Biology: Cellular Electron Microscopy, vol.79, pp. 769-798, Elsevier (2007); [3] Sandberg, K.: Curve enhancementusing orientation Fields. In Bebis, G. (ed.), Advances in VisualComputing, Part I. LNCS, vol. 5875, pp. 564-575, Springer, Heidelberg(2009); [4] Sandberg, K.: The Generalized Orientation Field Transform.In R. P. Barneva, et al. (ed.), Object Modeling, Algorithms, andApplications, pp. 107-112, Research Publishing (2010); [5] Sandberg, K.:The Curve Filter Transform—a Robust Method for Curve Enhancement. InBebis, G. (ed.), Advances in Visual Computing, Part II. LNCS, vol. 6454,pp. 107-116, Springer, Heidelberg (2010), each of which are incorporatedherein by reference.

The mathematical formulation of these components may be computed asdescribed herein below. Let I(x) denote the image intensity at location(pixel) x ⊂ D, where D denotes the image domain. For clarity, only grayscale images are considered in this presentation. The generalization tocolor images is straight forward by processing one or more channels of acolor image separately.

The orientation field is defined as:

F(x)=(x),θ(x))

where as shown in FIG. 53 w denotes a real-valued weight (step 5302) andθ denotes an angle (step 5304) in the interval [0,π). A criticalproperty is that the angle is defined for angles in the interval [0;180°), rather than the more traditional choice [0; 360°). This reflectsthe fact that the notion of a “forward” or “backward” direction for ourorientation is removed. The weight should have the property that |w(x)|measures the reliability of the orientation angle θ(x) at location x.

The mathematics associated with the orientation field generation are asfollows. Let r_(φ)(x, s) denote a parameterized straight line of lengthL and angle φ centered at x such that

$\begin{matrix}{{{r_{\phi}\left( {x,s} \right)} = {x + {s\; \cos \; \phi \hat{x}} + {s\; \sin \; \phi \hat{y}}}},{s \in \left\lbrack {{- \frac{L}{2}},\frac{L}{2}} \right\rbrack}} & (1)\end{matrix}$

Here {circumflex over (x)} and ŷ denote the unit vectors in the x- andy-directions, respectively.

We define the orientation spectrum function as:

${O\left( {x,\phi} \right)} = {\int_{- \frac{L}{2}}^{\frac{L}{2}}{{I\left( {r_{\phi}\left( {x,s} \right)} \right)}{ds}}}$

where L is a scale parameter. This integral measures the image intensityalong a straight line of angle φ, length L, centered at pixel x in theimage.

The orientation field F(x) is generated by the formula:

θ(x)=arg max_(∅∈[0,π))∫₀ ^(π) K(ϕ,φ)0(x,φ)dφ

w(x)=max_(∅∈[0,π))∫₀ ^(π) K(ϕ,φ)0(x,φ)dφ  (2)

where K(ϕ, φ) is a symmetric kernel, with the property

K(ϕ,ϕ)≤K(ϕ,φ),φ∈[0,π)

Examples of useful kernels include:

K _(δ)(ϕ,φ)≡δ(ϕ−φ)  (3)

where δ denotes the Dirac delta function, and

K _(cos(ϕ,φ))≡cos(2(ϕ−φ))  (4)

The Generalized Curve Field Transform may be used for more generalcurves. To accommodate looking for more general curves (not necessarilystraight lines), a general family of curves is denoted as:

{r _(λ)(x,s)}_(λ∈Λ)  (5)

Here Λ denote a set of curve parameters (e.g., angles and curvature),while

$\begin{matrix}{s \in \left\lbrack {{- \frac{L}{2}},\frac{L}{2}} \right\rbrack} & \;\end{matrix}$

is a parameterization of the curve such that r_(λ)(x, 0)=x. A usefulclass of curves is the family of all straight lines and circular arcs ofvarying curvature.

The main idea is to measure the alignment of an underlying orientationfield and a family of curves. The alignment at a pixel x is computed bycos(2(θ(x)−v_(k))), where θ(x) represents an orientation angle and v_(k)represents the tangent angle of a curve. This alignment measure isintegrated along a family of curves, which leads to the followingdefinition of the alignment spectrum of the orientation field F(w(x),θ(x)):

$\begin{matrix}{{{G\lbrack F\rbrack}\left( {x,\lambda} \right)} = {\int_{- \frac{L}{2}}^{\frac{L}{2}}{{w\left( {r_{\lambda}\left( {x,s} \right)} \right)}{\cos\left( {\left( {2\left( {{\theta \left( {r_{\lambda}\left( {x,s} \right)} \right)} - {v_{\lambda}\left( {x,s} \right)}} \right)} \right){\frac{{dr}_{\lambda}}{ds}}{ds}} \right.}}}} & (6)\end{matrix}$

For a given curve r_(λ), this function measures the net alignment ofthis curve and the underlying orientation field F through a pixel x (seeFIG. 54). FIG. 54 illustrates the alignment spectrum at the center pixelx₀ of a region covering the orientation field F of a straight(horizontal) line with a thickness of 6 pixels. In this example, we havethat

${{{G\lbrack\mathcal{F}\rbrack}\left( {x_{0},0} \right)} = 15},{{G{\mathcal{F}}\left( {x_{0},\frac{\pi}{4}} \right)} = 2.2},{{{and}\mspace{14mu} {G\lbrack\mathcal{F}\rbrack}\left( {x_{0},\frac{\pi}{2}} \right)} = 0.}$

Note that the net alignment can also be negative, which would happen for0=0 at a location a few pixels below the center pixel in this example.

Note that the family of curves r_(λ), is in general different from thefamily of curves used to generate the orientation field F. The original(or primary) orientation field F is computed using the family ofstraight lines. The reason for this is that it is typically better touse a more local scale for the primary orientation field, such that itonly looks for locally straight lines. When looking for curved lines, wecan use a family of lines r₂ that uses longer curves, and also allowthese curves to include lines that are not straight.

We introduce the curve parameter kernel P_(F(x))(y,λ) provides a rulefor how to weight the contribution of a curve labeled by λ. Note that weallow this kernel to depend on the orientation field F(x), which allowsthe use of non-linear kernels. Secondly, the spread kernelS_(G[F](y,λ))(x), is introduced which determines to which neighboringpixels to add information about the data collected along the curvelabeled by λ. For clarity, we will typically drop the reference to F inthe expression for S, and use the notation S_(G(y,λ))(x).

Given these definitions, the Generalized Curve Field Transform (GCFT) isdefined as:

C[F,G](x)=∫_(D) S _(G(y,λ))(x)(∫_(D)∫_(Λ) P_(F(y))(z,λ)G[F](z,λ)dzdλ)dy  (7)

where D denotes the domain of the image. Although this expression mayseem cumbersome, in many important applications, the kernels involvedelta functions that simplify the final expression.

The OFT used in [1] (Sandberg, K., Brega, M.: Segmentation of thinstructures in electron micrographs using orientation Fields. J. Struct.Biol. 157, 403-415 (2007)) corresponds to the following choices inequations (2) and (7):

Λ = [0, π):  Family  of  straight  lines  (see  Equation  (1)).K(φ, ϕ) = δ(φ − ϕ)${{G\lbrack F\rbrack}\left( {x,\varphi} \right)} = {\int_{- \frac{L}{2}}^{\frac{L}{2}}{{w\left( {r_{\varphi}\left( {x,s} \right)} \right)}{\cos \left( {2\left( {{\theta \left( {r_{\varphi}\left( {x,s} \right)} \right)} - \varphi} \right)} \right)}{ds}}}$${{P_{F{(y)}}\left( {z,\varphi} \right)} = {{\delta \left( {y - z} \right)}{\delta \left( {\varphi - {\varphi_{\max}(y)}} \right)}}},{{{where}\mspace{14mu} {\varphi_{\max}(y)}} = {\arg {\max\limits_{\varphi \in {\lbrack{0,\pi})}}{{F\left( {y,\varphi} \right)}}}}}$S_(G(y, φ))(x) = δ(x − y)

Using these kernels in Equation (7) gives:

${{C\left\lbrack {F,G} \right\rbrack}(x)} = {\int_{- \frac{L}{2}}^{\frac{L}{2}}{{w\left( {r_{\varphi_{\max}(x)}\left( {x,s} \right)} \right)}{\cos \left( {2\left( {{\theta \left( {r_{\varphi_{\max}(x)}\left( {x,s} \right)} \right)} - {\varphi_{\max}(x)}} \right)} \right)}{ds}}}$

where ϕ_(max)(x)=arg max_(ϕ∈[0,π))|F(x,ϕ)|. This integral will measurethe best net alignment for any angle through pixel x. For the example inFIG. 54, the result for the center pixel would be 15.

The CFT used in [5] (Sandberg, K.: The Curve Filter Transform—a RobustMethod for Curve Enhancement. In Bebis, G. (ed.), Advances in VisualComputing, Part II. LNCS, vol. 6454, pp. 107-116, Springer, Heidelberg(2010)), each of which are incorporated herein by reference correspondsto the following choices in equations (2) and (7):

-   -   Λ: Family of circular arcs of varying curvature labeled by their        tangent angles θ ∈ [0,2π) and curvature κ ∈ (0, κ) where κ is a        user specified maximum curvature. For clarity in the notation, a        single parameter λ≡(θ, κ) denotes an individual curve.

K(φ, ϕ) = cos (2(φ − ϕ))${{G\lbrack F\rbrack}\left( {x,\lambda} \right)} = {\int_{- \frac{L}{2}}^{\frac{L}{2}}{{w\left( {r_{\lambda}\left( {x,s} \right)} \right)}{\cos \left( {2\left( {{\theta \left( {r_{\lambda}\left( {x,s} \right)} \right)} - {v_{\lambda}(s)}} \right)} \right)}{\frac{{dr}_{\lambda}}{ds}}{ds}}}$P_(F(y))(z, λ) = δ(y − z)δ(λ − λ_(max)(y))  ${{where}\mspace{14mu} {\lambda_{\max}(y)}} = {\arg {\max\limits_{{({\varphi,k})} \in {{\{{0,{2\pi}})} \times {({0,\kappa})}}}{{F\left( {y,\left( {\varphi,\kappa} \right)} \right)}}}}$S_(G(y, λ))(x) = χ_(R_(λ_(max))(y))(x)

where R_(λ) _(max) (y) is a set defined as

${R_{\lambda_{\max}}(y)} = \left\{ {{{z \in D}{\exists{s \in {\left\lbrack {{- \frac{L}{2}},\frac{L}{2}} \right\rbrack \text{:}\mspace{14mu} z}}}} = {r_{\lambda_{\max}{(y)}}\left( {y,s} \right)}} \right\}$

and χ denotes the characteristic function such that

${\chi_{R_{\lambda_{\max}}{(y)}}(x)} = \left\{ \begin{matrix}{{1\mspace{14mu} {if}\mspace{14mu} x} \in {R_{\lambda_{\max}}(y)}} \\{{0\mspace{14mu} {if}\mspace{14mu} x} \notin {R_{\lambda_{\max}}(y)}}\end{matrix} \right.$

Using these kernels gives

C[F, G](x) = ∫_(D)χ_(R_(λ_(max))(y))(x)G[F](y, λ_(max)(y))dy${{where}\mspace{14mu} {\lambda_{\max}(y)}} = {\arg {\max\limits_{{({\varphi,k})} \in {{\{{0,{2\pi}})} \times {({0,k})}}}{{F\left( {y,\left( {\varphi,\kappa} \right)} \right)}}}}$

The choices for the CFT can be modified to generate a slightly differentcurve enhancement transform. For example, if P_(F(y))(z,λ)=1 (a constantfunction) is selected, a more precise enhancement of curved structurescan sometimes be achieved.

If the same family of curves as for the CFT is used, butP_(F(y))(z,(θ,κ))=δ(κ) is selected, we effectively single out straightlines (with curvature κ=0) in the final result. IfP_(F(y)(z,(θ,κ))=)δ(κ−κ₀) is chosen, curves of constant curvature κ₀ aresingled out.

For this example, it might seem that if we want to detect structure of aspecific curvature, we should only use lines of that curvature in ourcurve family to begin with. However, we are better off by still usingcurves of all curvatures for generating the curve field, and single outspecific lines by the choice P_(F(y))(z,(θ,κ))=δ(κ−κ₀). That way weensure that we first “label” all curved lines in the image as accuratelyas possible with the correct curvature, and then single out the specificcurves we are interested in.

In contrast, if we are looking for curves with curvature κ₀, and onlyuse such curves in the curve family r_(λ), then curves of a similarcurvature, say κ₁=κ₀+ϵ will also get a fairly high response. If a curvefamily including curves with both curvature k₀ and κ₀+ϵ is used, thencurves with a curvature closer to κ₁=κ₀+ϵ will get a higher response forthe curve parameter k₁ and be labeled as such. Higher precision may beachieved when identifying curves of a specific curvature by using alarger family of curves. However, using a larger family of curves alsocomes with a performance penalty, and is therefore not always worth theeffort. This illustrates the use of GCFT to selectively filter outcurves of different geometry.

Test Pad Detection

The system can be implemented in a number of applications such as testpad detection, location of a test strip, locating test pads within astrip and drug cup feature extraction. These will be more describedherein below.

FIG. 55 illustrates how two test pads can be located on a test strip inan image. The process consists of two steps. First the strip is locatedat step 5502 using a coarse scale for the entire image. Next, theboundaries of two test pads are located at step 5504 within the stripfound in step 5502 using a finer scale on a smaller region of the fullimage. This achieves both robustness and high computational performance.

In order to locate the test strip at step 5502, we are specificallylooking for straight lines. Also, since the lines are relatively thickline and is known from the auto-capture to extend throughout asignificant portion of the image, a relatively coarse scale may be used(which is specified via the parameter L that is used for the limits ofintegration). Note that since L is large as we are looking at coarsescale, the underlying image can be down sampled to reduce the size ofthe problem for increased computational efficiency.

The following kernels can be used:

Λ = [0, π):  Family  of  straight  lines  (see  Equation  (1)).K(φ, ϕ) = cos (2(φ − ϕ))${{{G\lbrack F\rbrack}\left( {x,\varphi} \right)} = {{\int_{- \frac{L}{2}}^{\frac{L}{2}}{{w\left( {r_{\varphi}\left( {x,s} \right)} \right)}{\cos \left( {2\left( {{\theta \left( {r_{\varphi}\left( {x,s} \right)} \right)} - \varphi} \right)} \right)}{ds}{P_{F{(y)}}\left( {z,\varphi} \right)}}} = {{H_{\varphi_{\max}{(y)}}\left( {y,z} \right)}{\cos \left( {2\left( {\varphi - {\varphi_{\max}(y)}} \right)} \right)}}}},{{{where}\mspace{14mu} {\varphi_{\max}(y)}} = {\arg {\max\limits_{\varphi \in {\lbrack{0,\pi})}}{{F\left( {y,\varphi} \right)}}}}}$

H_(ϕ)(x, y) is a kernel illustrated in FIG. 56. The white pixels have avalue 1, gray pixels have a value 0 and the black pixels have a value−1. The kernel for the other angles θ can be generated by rotatingH_(ϕ)(x, y) by θ degrees. This kernel is particularly useful foridentifying lines of a specific scale.

S_(G(y,ϕ))(x)=χ_(R) _(ϕmax) _((y))(x) where R_(ϕ) _(max) (y) is a setdefined as

${R_{\lambda_{\max}}(y)} = \left\{ {{{z \in D}{\exists{s \in {\left\lbrack {{- \frac{L}{2}},\frac{L}{2}} \right\rbrack \text{:}\mspace{14mu} z}}}} = {r_{\varphi_{\max}{(y)}}\left( {y,s} \right)}} \right\}$

and χ denotes the characteristic function such that

${\chi_{R_{\varphi_{\max}}{(y)}}(x)} = \left\{ \begin{matrix}{{1\mspace{14mu} {if}\mspace{14mu} x} \in {R_{\varphi_{\max}}(y)}} \\{{0\mspace{14mu} {if}\mspace{14mu} x} \notin {R_{\varphi_{\max}}(y)}}\end{matrix} \right.$

In this application, the occurrence of many curved lines is notanticipated, and for performance reasons only a family of straight linesis used.

The choice of kernel for P leads to an interesting and useful propertysuch that lines with a thickness that match the scale of the kernel willgive a strong response, and can be shown to be stronger than the maximumresponse that can be obtained for a step edge. This is illustrated inFIG. 57 where the strip line has a stronger amplitude than the otherwisewell-defined step edges in other parts of the image. FIG. 57 shows theoutput of the GCFT using a coarse scale for locating the UTI strip inFIG. 50. The horizontal centerline 5702 indicates the location and angleof the UTI strip.

Once the strip has been identified at step 5502, The GCFT is used atstep 5504 to locate straight lines of a finer scale. FIG. 58 illustratesan example of the result. FIG. 58 shows the output of GCFT applied tothe image in FIG. 50. Despite the week edge between the test pad and thebackground, the GCFT is able to enhance the edges without enhancingstray edges from the background. Stray edges are otherwise a commonproblem with traditional edge detection methods when applied to imageswith low contrast edges.

Drug Cup Feature Extraction

In order to identify the strip windows on a drug cup under a widevariety of lighting conditions, some prominent coarse line features ofthe drug cup using the GCFT must first be identified. By analyzing themutual distances and angles between different lines, we can determinewhich two lines that correspond to the stickers above and below the teststrip region (see FIG. 59). FIG. 59 illustrates the two lines that matchthe signature of the two stickers above and below the strip region. Oncethe strip region has been identified, the edges within the strip regionare enhanced, as illustrated in FIG. 60. In addition to identifying thelines, the GCFT also allows the classification of the edges by theirorientations, which are illustrated by marking edge points as black(horizontal lines 6002) and white (vertical lines 6004) in FIG. 60.

While the above discussion has more generally described the imageanalysis process, alternative embodiments and processes are describedbelow. Referring now to FIG. 61, there is generally illustrated theprocess for improving analysis of the testing results describedhereinabove when an image representation of the test results is createdand then analysis of the results must be carried out in order todetermine what the test results are indicating. The test result image6102 that has been digitally created from a view of the test results insome manner is provided to an image processing system 6104. The imageprocessing system 6104 receives the test results image 6104 andprocesses the image in order to create a clearer more easily analyzedimage of the test results. A test results analysis 6108 may be providedthat provides a diagnosis of the patient.

Referring now to FIG. 62, there is provided a more detailed flow diagramof the image processing performed by the image processing system 6104 ofFIG. 61. Initially, the test is performed at step 6202 on the testingitem which may comprise a testing device, microfluidic cassette, or anyother type of device that may visually display test results. Next, animage of the test results is generated at step 6204. The generation ofthe image of test results may be accomplished using for example a smartphone, a dedicated image capture system, or any other or device capableof generating a digital image of the test results as described hereinthat may be captured and forwarded for further processing. The generatedimage of the digital test results from step 6204 are input at step 6206to the image processing system 6104 for improvement and clarification ofthe image. The input image data is processed at step 6208 to removenoise and other distortions from the digital image. A comparativeanalysis is performed at step 6210 of the process digital image. Theparticular test results are determined at step 6212 based upon theprocessed image test data. These results are output for action at step6214.

Referring now to FIG. 63, there is provided a flow diagram giving adetailed illustration of the image processing of the image of thedigital test results in order to remove noise and other distortions fromthe generated image. A user conducted self-test is performed at step6302 using a test device. The test device may comprise a microfluidiccassette, test strip or other type of testing device as disclosed hereinabove. The test results are captured at step 6304 with a digital imageto provide a captured view of the test results. The generated testresults are bound at step 6306 with a trackable ID that is associatedwith the particular test results. The type of testing device, i.e.microfluidic cassette, test strip, etc., is recognized at step 6308, andthe recognized testing device is also bound with the trackable ID atstep 6310. The first step involves the assigning of an angle to eachpixel of the image of the test results at step 6312.

The establishment of the angle of step 6312 is more particularlyillustrated in the flow diagram in FIG. 64. First a pixel is selectedfor testing at step 6402. A first line is established at step 6404 forthe selected pixel that has a particular orientation. An intensityresponse of the established line is measured at step 6406. Once theintensity measurements for the previous line have been made at step6406, inquiry step 6408 determines if there is a further line possiblefor the selected pixel. If so, a next line is established at step 6410and control passes back to step 6406 to measure the intensity responseof the next line. This process continues until an intensity response foreach possible line associated with the pixel is determined. When inquirystep 6408 determines that another line is not available, control passesto step 6412 to determine the response for each of the measured lines.The line is selected using the process described with respect toequation (2) described herein above as the orientation line for thecurrent pixel at step 6414. The process used in steps 6412 and 6414would use in one example select a line having the highest intensityresponse. Inquiry step 6416 determines if further pixels exist withinthe image, and if so, control passes back to step 6402 for the nextpixel. If inquiry step 6416 determines no further image pixels areavailable, the process is completed at step 6418.

Referring now back to FIG. 63, once the images been completelytransformed and assigned orientation at step 6316, the relevantgeometric shapes which need to be located for within the image, areselected at step 6318. The alignment spectrum image is created at step6322. the image may be filtered at step 6324 using the generalized curvefield transform to remove undesired shapes and edge signatures from theimage using any number of available techniques. These shapes and edgesignatures may be selected to be consistent with things such as shadows,smudges, glare, etc. that interfere with the actual image of the testresults that is trying to be analyzed. The filtering process is morefully described with respect to FIG. 65. The generated alignmentspectrum image is received at step 6502. The various geometricshapes/items to be filtered from the alignment spectrum image are inputat step 6504 so that they may be subtracted/removed/filtered from thealignment spectrum image. The geometric shapes are located andsubtracted from the alignment spectrum image at step 6506.

Referring now back to FIG. 63, once the undesired features have beenfiltered from the alignment spectrum image at step 6324, the relevantgeometric features are identified at step 6326. This may compriseinformation such as lines or curves that indicate important testresults.

Inquiry step 6330 determines if further image correction is needed andif so, the image is appropriately corrected at step 6332. If no furtherimage correction is needed or after correction of the image at step6332, the processed image is output at step 6334, and a number ofdifferent manners for utilizing the processed image result may thenoccur. The output image data can act as a trigger 6336 for initiating anext process in a testing methodology. This could include activating adoctor consultation starting generation of a treatment protocol, etc.Additionally, the processed image can be utilized as part of a machinelearning process 6344 providing a diagnosis or adding to data relatingto disease analysis.

Utilizing the above described techniques, digital images created of testresults from a variety of testing processes may be image processed inorder to provide a better analysis of the provided test results. Noise,shadows, smudges and other factors that would blur or distort images oftest results may be removed from and image of a test result in order toprovide more accurate test analysis. This provides great enhancements toonline diagnosis and testing methodologies that inherently require thetransmission of digital data as part of the process.

It should be understood that the drawings and detailed descriptionherein are to be regarded in an illustrative rather than a restrictivemanner, and are not intended to be limiting to the particular forms andexamples disclosed. On the contrary, included are any furthermodifications, changes, rearrangements, substitutions, alternatives,design choices, and embodiments apparent to those of ordinary skill inthe art, without departing from the spirit and scope hereof, as definedby the following claims. Thus, it is intended that the following claimsbe interpreted to embrace all such further modifications, changes,rearrangements, substitutions, alternatives, design choices, andembodiments.

What is claimed is:
 1. A method for image processing medical self-testresults, comprising: receiving a self-test result that provides a visualindication of a test result; generating a digital image of the visualindication of the test result, the visual image including noise anddistortions therein; processing the digital image using generalizedcurve field transforms to extract relevant features of the digital imagein a presence of the noise and distortions to create a transformedimage; generating a diagnosis based upon the transformed image; anddisplaying the diagnosis.
 2. The method of claim 1, wherein the step ofprocessing further comprises: generating an alignment spectrum of thedigital image; generating a generalized curve field transformation ofthe digital image from the alignment spectrum; determining a presence ofthe at least one geometric shape within the alignment spectrum image. 3.The method of claim 2 further comprising the step of filtering thealignment spectrum image to remove at least one predetermined geometricshape.
 4. The method of claim 3 further including the step ofdetermining a presence of a second at least one predetermined geometricshape within the alignment spectrum image.
 5. The method of claim 2,wherein the step of generating an orientation field further comprises:determining for each pixel within the digital image an orientation linefrom a plurality of possible lines based upon an intensity response forthe each line; and determining for each pixel within the digital image areliability factor for the determined orientation line.
 6. The method ofclaim 1, wherein the step of processing further comprises removingshadows from the digital image to create the transformed image.
 7. Amethod for image processing medical self-test results, comprising:receiving a digital image of a visual indication of a test result, thevisual image including noise and distortions therein; processing thedigital image using generalized curve field transforms to extractrelevant features of the digital image in a presence of the noise anddistortions to create a transformed image; and generating a diagnosisbased upon the transformed image.
 8. The method of claim 7, wherein thestep of processing further comprises: generating an alignment spectrumof the digital image; generating a generalized curve fieldtransformation of the digital image from the alignment spectrum;determining a presence of the at least one geometric shape within thealignment spectrum image.
 9. The method of claim 8 further comprisingthe step of filtering the alignment spectrum image to remove at leastone predetermined geometric shape.
 10. The method of claim 9 furtherincluding the step of determining a presence of a second at least onepredetermined geometric shape within the alignment spectrum image. 11.The method of claim 8, wherein the step of generating an orientationfield further comprises: determining for each pixel within the digitalimage an orientation line from a plurality of possible orientation linesbased upon an intensity response for the orientation line; anddetermining for each pixel within the digital image a reliability factorfor the determined orientation line.
 12. The method of claim 7, whereinthe step of processing further comprises removing shadows from thedigital image to create the transformed image.
 13. A method for imageprocessing medical self-test results, comprising: receiving a self-testresult that provides a visual indication of a test result; generating adigital image of the visual indication of the test result, the visualimage including noise and distortions therein; generating an alignmentspectrum of the digital image; generating a generalized curve fieldtransformation of the digital image from the alignment spectrum;determining a presence of the at least one geometric shape within thealignment spectrum image; filtering the alignment spectrum image toremove at least one predetermined geometric shape; generating adiagnosis based upon the processed test results; and displaying thediagnosis.
 14. The method of claim 13 further including the step ofdetermining a presence of a second at least one predetermined geometricshape within the alignment spectrum image.
 15. The method of claim 13,wherein the step of generating an orientation field further comprises:determining for each pixel within the digital image an orientation linefrom a plurality of possible orientation lines based upon an intensityresponse for the orientation line; and determining for each pixel withinthe digital image a reliability factor for the determined orientationline.
 16. The method of claim 13, wherein the step of processing furthercomprises removing shadows from the digital image to create thetransformed image.
 17. The method of claim 13, wherein the step ofgenerating the alignment spectrum image further comprises: